Inpainting dental images with missing anatomy

ABSTRACT

Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are processed using a second machine learning model to label anatomy. The anatomy labels, teeth labels, and image are processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, labels, and image may be input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. A machine learning model may be trained with images and randomly generated masks in order to perform inpainting of dental images with missing information.

RELATED APPLICATIONS

This application is a continuation in part of U.S. application Ser. No.16/875,922 filed May 15, 2020 and entitled ARTIFICIAL INTELLIGENCEARCHITECTURE FOR IDENTIFICATION OF PERIODONTAL FEATURES.

This application claims the benefit of the following applications, allof which are hereby incorporated herein by reference in their entirety:

U.S. Provisional Application Ser. No. 62/867,817 filed Jun. 27, 2019,and entitled SYSTEM AND METHODS FOR AUTOMATED CARIES CLASSIFICATION,SCORING, QUANTIFICATION, AND INSURANCE CLAIMS ADJUDICATION.

U.S. Provisional Application Ser. No. 62/868,864 filed Jun. 29, 2019,and entitled SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE-BASEDDENTAL IMAGE TO TEXT GENERATION.

U.S. Provisional Application Ser. No. 62/868,870 filed Jun. 29, 2019,and entitled AN AUTOMATED DENTAL PATIENT IDENTIFICATION PLATFORM.

U.S. Provisional Application Ser. No. 62/916,966 filed Oct. 18, 2019,and entitled SYSTEMS AND METHODS FOR AUTOMATED ORTHODONTIC RISKASSESSMENT, MEDICAL NECESSITY DETERMINATION, AND TREATMENT COURSEPREDICTION.

FIELD OF THE INVENTION

This invention relates to automating the analysis of dental images.

BACKGROUND

The field of dentistry relates to a broad range of oral healthcare,which are often discretized into several sub-fields such as disease ofthe bone (periodontitis), disease of the tooth (caries), or bone andtooth alignment (orthodontics). Although these sub-fields are unique andclinicians undergo special training to specialize in these sub-fields,they share some commonalities. Although different image modalities arefavored in sub-fields more than others, all sub-fields utilize similarimaging strategies such as full mouth series (FMX), cone-beam computedtomography (CBCT), cephalometric, panoramic, and intra-oral images. Allsub-fields of dentistry use images for assessment of patientorientation, anatomy, comorbidities, past medical treatment, age,patient identification, treatment appropriateness, and time seriesinformation.

Diagnosis of disease in the dental field is performed by visualinspection of dental anatomy and features and by analysis of imagesobtained by X-ray or other imaging modality. There have been someattempts made to automate this process.

BRIEF DESCRIPTION OF THE FIGURES

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1 is a process flow diagram of a method for classifying treatmentin accordance with an embodiment of the present invention;

FIG. 2 is a process flow diagram of a hierarchy for classifying atreatment;

FIG. 3 is a schematic block diagram of a system for identifying imageorientation in accordance with an embodiment of the present invention;

FIG. 4 is a schematic block diagram of a system for classifying imagesof a full mouth series in accordance with an embodiment of the presentinvention;

FIG. 5 is a schematic block diagram of a system for removing imagecontamination in accordance with an embodiment of the present invention;

FIG. 6A is a schematic block diagram of system for performing imagedomain transfer in accordance with an embodiment of the presentinvention;

FIG. 6B is a schematic block diagram of a cyclic GAN for performingimage domain transfer in accordance with an embodiment of the presentinvention;

FIG. 7 is a schematic block diagram of a system for labeling teeth in animage in accordance with an embodiment of the present invention;

FIG. 8 is a schematic block diagram of a system for labeling periodontalfeatures in an image in accordance with an embodiment of the presentinvention;

FIG. 9 is a schematic block diagram of a system for determining clinicalattachment level (CAL) in accordance with an embodiment of the presentinvention;

FIG. 10 is a schematic block diagram of a system for determining pocketdepth (PD) in accordance with an embodiment of the present invention;

FIG. 11 is a schematic block diagram of a system for determining aperiodontal diagnosis in accordance with an embodiment of the presentinvention;

FIG. 12 is a schematic block diagram of a system for restoring missingdata in images in accordance with an embodiment of the presentinvention;

FIG. 13 is a schematic block diagram of a system for detectingadversarial images in accordance with an embodiment of the presentinvention;

FIG. 14A is a schematic block diagram of a system for protecting amachine learning model from adversarial images in accordance with anembodiment of the present invention;

FIG. 14B is a schematic block diagram of a system for training a machinelearning model to be robust against attacks using adversarial images inaccordance with an embodiment of the present invention;

FIG. 14C is a schematic block diagram of a system for protecting amachine learning model from adversarial images in accordance with anembodiment of the present invention;

FIG. 14D is a schematic block diagram of a system for modifyingadversarial images to protect a machine learning model from corruptedimages in accordance with an embodiment of the present invention;

FIG. 14E is a schematic block diagram of a system for dynamicallymodifying a machine learning model to protect it from adversarial imagesin accordance with an embodiment of the present invention;

FIG. 15 is a schematic block diagram illustrating the training of amachine learning model at a plurality of disparate institutions inaccordance with an embodiment of the present invention;

FIG. 16 is a process flow diagram of a method for generating a combinedstatic model from a plurality of disparate institutions in accordancewith an embodiment of the present invention;

FIG. 17 is a schematic block diagram illustrating the training of acombined static model by a plurality of disparate institutions inaccordance with an embodiment of the present invention;

FIG. 18 is a process flow diagram of a method for training a moving basemodel for a plurality of disparate institutions in accordance with anembodiment of the present invention;

FIG. 19 is a schematic block diagram of a system for combing gradientsfrom a plurality of disparate institutions;

FIG. 20 is a schematic block diagram illustrating dental anatomy;

FIG. 21 is a schematic block diagram of a system for identifyingperturbations to anatomy labels in accordance with an embodiment of thepresent invention;

FIG. 22 is a schematic block diagram of another system for identifyingperturbations to anatomy labels in accordance with an embodiment of thepresent invention;

FIG. 23 is a schematic block diagram of a system for identifying cariesbased on anatomy labeling style in accordance with an embodiment of thepresent invention;

FIG. 24 is a schematic block diagram of a system for detecting defectsin a restoration in accordance with an embodiment of the presentinvention;

FIG. 25 is a schematic block diagram of a system for selecting arestoration for a tooth in accordance with an embodiment of the presentinvention;

FIG. 26 is a schematic block diagram of a system for identifyingsurfaces of a tooth having caries in accordance with an embodiment ofthe present invention;

FIG. 27 is a schematic block diagram of a system for selecting dentaltreatments in accordance with an embodiment of the present invention;

FIG. 28 is a schematic block diagram of a system for selecting adiagnosis, treatment, or patient match in accordance with an embodimentof the present invention;

FIG. 29 is a schematic block diagram of a system for predicting claimadjudication in accordance with an embodiment of the present invention;

FIG. 30 is a schematic block diagram of a system for predicting atreatment being appropriate based on past treatment in accordance withan embodiment of the present invention;

and

FIG. 31 is a schematic block diagram of a computer system suitable forimplementing methods in accordance with embodiments of the presentinvention.

DETAILED DESCRIPTION

It will be readily understood that the components of the invention, asgenerally described and illustrated in the Figures herein, could bearranged and designed in a wide variety of different configurations.Thus, the following more detailed description of the embodiments of theinvention, as represented in the Figures, is not intended to limit thescope of the invention, as claimed, but is merely representative ofcertain examples of presently contemplated embodiments in accordancewith the invention. The presently described embodiments will be bestunderstood by reference to the drawings, wherein like parts aredesignated by like numerals throughout.

Embodiments in accordance with the invention may be embodied as anapparatus, method, or computer program product. Accordingly, theinvention may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, the invention may take the form of a computerprogram product embodied in any tangible medium of expression havingcomputer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readablemedia may be utilized. For example, a computer-readable medium mayinclude one or more of a portable computer diskette, a hard disk, arandom access memory (RAM) device, a read-only memory (ROM) device, anerasable programmable read-only memory (EPROM or Flash memory) device, aportable compact disc read-only memory (CDROM), an optical storagedevice, and a magnetic storage device. In selected embodiments, acomputer-readable medium may comprise any non-transitory medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

Computer program code for carrying out operations of the invention maybe written in any combination of one or more programming languages,including an object-oriented programming language such as Java,Smalltalk, C++, or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages, and may also use descriptive or markup languages such asHTML, XML, JSON, and the like. The program code may execute entirely ona computer system as a stand-alone software package, on a stand-alonehardware unit, partly on a remote computer spaced some distance from thecomputer, or entirely on a remote computer or server. In the latterscenario, the remote computer may be connected to the computer throughany type of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

The invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions or code. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in anon-transitory computer-readable medium that can direct a computer orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

Referring to FIG. 1, a method 100 may be performed by a computer systemin order to select an outcome for a set of input data. The outcome maybe a determination whether a particular course of treatment is corrector incorrect. The method 100 may include receiving 102 an image. Theimage may be an image of patient anatomy indicating the periodontalcondition of the patient. Accordingly, the image may be of a of apatient's mouth obtained by means of an X-ray (intra-oral or extra-oral,full mouth series (FMX), panoramic, cephalometric), computed tomography(CT) scan, cone-beam computed tomography (CBCT) scan, intra-oral imagecapture using an optical camera, magnetic resonance imaging (MRI), orother imaging modality.

The method 100 may further include receiving 104 patient demographicdata, such as age, gender, underlying health conditions (diabetes, heartdisease, cancer, etc.). The method 100 may further include receiving 106a patient treatment history. This may include a digital representationof periodontal treatments the patient has received, such as cleanings,periodontal scaling, root planing, fillings, root canals, orthodontia,oral surgery, or other treatments or procedures performed on the teeth,gums, mouth, or jaw of the patient.

The method 100 may include pre-processing 108 the image received at step102. Note that in some embodiments, the image received is correctlyoriented, obtained using a desired imaging modality, and free ofcontamination or defects such that pre-processing is not performed. Inother embodiments, some or all of re-orienting, removing contamination(e.g., noise), transforming to a different imaging modality, andcorrecting for other defects may be performed at step 108. In someembodiments, step 108 may correct for distortion due to foreshortening,elongation, metal artifacts, and image noise due to poor imageacquisition from hardware, software, or patient setup.

Step 108 may further include classifying the image, such as classifyingwhich portion of the patient's teeth and jaw is in the field of view ofthe image. For example, a full-mouth series (FMX) typically includesimages classified as Premolar2, Molar3, Anterior1, Anterior2, Anterior3and their respective corresponding locations such as Jaw Region,Maxilla, and Mandible. For each of these, the view may be classified asbeing the left side or right side of the patients face.

In the following description reference to an “image” shall be understoodto interchangeably reference either the original image from step 102 oran image resulting from the pre-processing of step 108.

The method 100 may further include processing 110 the image to identifypatient anatomy. Anatomy identified may be represented as a pixel maskidentifying pixels of the image that correspond to the identifiedanatomy and labeled as corresponding to the identified anatomy. This mayinclude identifying individual teeth. As known in the field ofdentistry, each tooth is assigned a number. Accordingly, step 110 mayinclude identifying teeth in the image and determining the number ofeach identified teeth. Step 110 may further include identifying otheranatomical features for each identified tooth, such as itscementum-enamel junction (CEJ), boney points corresponding toperiodontal disease around the tooth, gingival margin (GM), junctionalepithelium (JE), or other features of the tooth that may be helpful incharacterizing the health of the tooth and the gums and jaw around thetooth.

The method 100 may further include detecting 112 features present in theanatomy identified at step 110. This may include identifying caries,measuring clinical attachment level (CAL), measuring pocket depth (PD),or identifying other clinical conditions that may indicate the need fortreatment. The identifying step may include generating a pixel maskdefining pixels in the image corresponding to the detected feature. Themethod 100 may further include generating 114 a feature metric, i.e. acharacterization of the feature. This may include performing ameasurement based on the pixel mask from step 112. Step 114 may furthertake as inputs the image and anatomy identified from the image at step110. For example, CAL or PD of teeth in an image may be measured, suchas using the machine-learning approaches described below (see discussionof FIGS. 9 and 10)

The result of steps 108, 110, 112, and 114 is an image that may havebeen corrected, labels, e.g. pixel masks, indicating the location ofanatomy and detected features and a measurement for each detectedfeature. This intermediate data may then be evaluated 116 with respectto a threshold. In particular, this may include an automated analysis ofthe detected and measured features with respect to thresholds. Forexample, CAL or PD measured using the machine-learning approachesdescribed below may be compared to thresholds to see if treatment may beneeded. Step 116 may also include evaluating some or all of the images,labels, detected features, and measurements for detected features amachine learning model to determine whether a diagnosis is appropriate(see FIG. 11).

If the result of step 116 is affirmative, then the method 100 mayinclude processing 118 the feature metric from step 114 according to adecision hierarchy. The decision hierarchy may further operate withrespect to patient demographic data from step 104 and the patienttreatment history from step 106. The result of the processing accordingto the decision hierarchy may be evaluated at step 120. If the result isaffirmative, than an affirmative response may be output 122. Anaffirmative response may indicate that the a course of treatmentcorresponding to the decision hierarchy is determined to be appropriate.If the result of processing 118 the decision hierarchy is negative, thenthe course of treatment corresponding to the decision hierarchy isdetermined not to be appropriate. The evaluation according to the method100 may be performed before the fact, i.e. to determine whether toperform the course of treatment. The method 100 may also be performedafter the fact, i.e. to determine whether a course of treatment that wasalready performed was appropriate and therefore should be paid for byinsurance.

FIG. 2 illustrates a method 200 for evaluating a decision hierarchy,such as may be performed at step 118. The method 200 may be a decisionhierarchy for determining whether scaling and root planing (SRP) shouldbe performed for a patient. SRP is performed in response to thedetection of pockets. Accordingly, the method 200 may be performed inresponse to detecting pockets at step 112 (e.g., pockets having aminimum depth, such as at least pocket having a depth of at least 5 mm)and determining that the size of these pockets as determined at step 114meets a threshold condition at step 116, e.g. there being at least onepocket (or some other minimum number of pockets) having a depth above aminimum depth, e.g. 5 mm.

The method 200 may include evaluating 202 whether the treatment, SRP,has previously been administered within a threshold time period prior toa reference time that is either (a) the time of performance of themethod 200 and (b) the time that the treatment was actually performed,i.e. the treatment for which the appropriateness is to be determinedaccording to the method 100 and the method 200. For example, this mayinclude whether SRP was performed within 24 months of the referencetime.

If not, the method 200 may include evaluating 204 whether the patient isabove a minimum age, such as 25 years old. If the patient is above theminimum age, the method 200 may include evaluating 206 whether thenumber of pockets having a depth exceeding a minimum pocket depthexceeds a minimum pocket number. For example, where the method 200 isperformed to determine whether SRP is/was appropriate for a quadrant(upper left, upper right, lower left, lower right) of the patient's jaw,step 206 may include evaluating whether there are at least four teeth inthat quadrant that collectively include at least 8 sites, each siteincluding a pocket of at least 5 mm. Where the method 200 is performedto determine whether SRP is/was appropriate for an area that is lessthan an entire quadrant, step 206 may include evaluating whether thereare one to three teeth that include at least 8 sites, each siteincluding a pocket of at least 5 mm.

If the result of step 206 is positive, then an affirmative result isoutput, i.e. the course of treatment is deemed appropriate. If theresult of step 206 is positive, then an affirmative result is output208, i.e. the course of treatment is deemed appropriate. If the resultof step 206 is negative, then a negative result is output 210, i.e. thecourse of treatment is deemed not to be appropriate.

If either of (a) SRP was found 202 to have been performed less than thetime window from the reference time or (b) the patient is found 204 tobe below the minimum age, the method 200 may include evaluating 212whether a periodontal chart has been completed for the patient within asecond time window from the reference time, e.g. six months. If theresult of step 212 is positive, then processing may continue at step206. If the result of step 212 is negative, then processing may continueat step 210.

The decision hierarchy of the method 200 is just one example. Decisionhierarchies for other treatments may be evaluated according to themethod 100, such as gingiovectomy; osseous mucogingival surgery; freetissue grafts; flap reflection or resection and debridement (with orwithout osseous recontouring); keratinized/attached gingivapreservation; alveolar bone reshaping; bone grafting (with or withoutuse of regenerative substrates); guided tissue regeneration; alveolarbone reshaping following any of the previously-mentioned procedures; andtissue wedge removal for performing debridement, flap adaptation, and/orpocket depth reduction. Examples of decision hierarchies for thesetreatments are illustrated in the U.S. Provisional Application Ser. No.62/848,905.

FIG. 3 is a schematic block diagram of a system 300 for identifyingimage orientation in accordance with an embodiment of the presentinvention. The illustrated system may be used to train a machine todetermine image orientation as part of the pre-processing of step 108 ofthe method 100. In particular, once an image orientation is known, itmay be rotated to a standard orientation for processing according tosubsequent steps of the method 100.

As described below, machine learning models, such as a CNN, may be usedto perform various tasks described above with respect to the method 100.Training of the CNN may be simplified by ensuring that the images usedare in a standard orientation with respect to the anatomy represented inthe images. When images are obtained in a clinical setting they areoften mounted incorrectly by a human before being stored in a database.The illustrated system 300 may be used to determine the orientation ofanatomy in an image such that they may be rotated to the standardorientation, if needed, prior to subsequent processing with another CNNor other machine learning model.

A training algorithm 302 takes as inputs training data entries that eachinclude an image 304 according to any of the imaging modalitiesdescribed herein and an orientation label 306 indicating the orientationof the image, e.g. 0 degrees, 90 degrees, 180 degrees, and 270 degrees.The orientation label 306 for an image may be assigned by a humanobserving the image and determining its orientation. For example, alicensed dentist may determine the label 306 for each image 304.

The training algorithm 302 may operate with respect to a loss function308 and modify a machine learning model 310 in order to reduce the lossfunction 308 of the model 310. In this case, the loss function 308 maybe a function that increases with a difference between the angleestimated by the model 310 for the orientation of an image 304 and theorientation label 306 of the image.

In the illustrated embodiment, the machine learning model 310 is aconvolution neural network. For example, the machine learning model 310may be an encoder-based densely-connected CNN with attention-gated skipconnections and deep-supervision. In the illustrated embodiment, the CNNincludes six multi-scale stages 312 followed by a fully connected layer314, the output 316 of the fully connected layer 314 being anorientation prediction (e.g. 0 degrees, 90 degrees, 180 degrees, or 270degrees).

In some embodiment, each multi-scale stage 312 may contain three 3×3convolutional layers, which may be paired with batch-normalization andleaky rectified linear units (LeakyReLU). The first and lastconvolutional layers of each stage 312 may be concatenated via denseconnections which help reduce redundancy within the CNN by propagatingshallow information to deeper parts of the CNN.

Each multi-scale network stage 312 may be downscaled by a factor of twoat the end of each multi-scale stage 312 by convolutional downsampling.The second and fourth multi-scale stages 312 may be passed throughattention gates 318 a, 318 b before being concatenated with the lastlayer. For example, the gating signal of attention gate 318 a that isapplied to the second stage 312 may be derived from the output of thefourth stage 312. The gating signal of attention gate 318 b that isapplied to the fourth stage 312 may be derived from the output of thesixth stage 312. Not all regions of the image 304 are relevant fordetermining orientation, so the attention gates 318 a, 318 b may be usedto selectively propagate semantically meaningful information to deeperparts of the CNN.

In some embodiments, the input image 304 to the CNN is a raw 64×64 pixelimage and the output 316 of the network is a likelihood score for eachpossible orientation. The loss function 308 may be trained withcategorical cross entropy which considers each orientation to be anorthogonal category. Adam optimization may be used during training whichautomatically estimates the lower order moments and helps estimate thestep size which desensitizes the training routine to the initiallearning rate.

In at least one possible embodiment, the images 304 are 3D images, suchas a CT scan. Accordingly, the 3×3 convolutional kernels of themulti-scale networks with 3×3×3 convolutional kernels. The output 316 ofthe CNN may therefore map to four rotational configurations 0, 90, 180,and 270 along the superior-inferior axis as well as one orthogonalorientation in the superior-inferior direction.

Because machine learning models may be sensitive to training parametersand architecture, for all machine learning models described herein,including the machine learning model 310, a first set of training dataentries may be used for hyperparameter testing and a second set oftraining data entries not included in the first set may be used toassess model performance prior to utilization.

The training algorithm 302 for this CNN and other CNNs and machinelearning models described herein may be implemented using PYTORCH.Training of this CNN and other CNNs and machine learning modelsdescribed herein may be performed using a GPU, such as NVIDIA's TESLAGPUs coupled with INTEL XEON CPUs. Other machine learning tools andcomputational platforms may also be used.

Generating inferences using this machine learning model 310 and othermachine learning models described herein may be performed using the sametype of GPU used for training or some other type of GPU or other type ofcomputational platform. In other embodiment, inferences using thismachine learning model 310 or other machine learning models describedherein may be generated by placing the machine learning model on anAMAZON web services (AWS) GPU instance. During deployment, a server mayinstantiate the machine learning model and preload the modelarchitecture and associated weights into GPU memory. A FLASK server maythen load an image buffer from a database, convert the image into amatrix, such as a 32-bit matrix, and load it onto the GPU. The GPUmatrix may then be passed through the machine learning model in the GPUinstance to obtain an inference, which may then be stored in a database.Where the machine learning model transforms an image or pixel mask, thetransformed image or pixel mask may be stored in an image array bufferafter processing of the image using the machine learning model. Thistransformed image or pixel mask may then be stored in the database aswell.

In the case of the machine learning model 310 of FIG. 3, the transformedimage may be an image rotated from the orientation determined accordingto the machine learning model 310 to the standard orientation. Themachine learning model 310 may perform the transformation or this may beperformed by a different machine learning model or process.

FIG. 4 is a schematic block diagram of a system 400 for determining theview of a full mouth series (FMX) that an image represents in accordancewith an embodiment of the present invention. The illustratedarchitecture may be used to train a machine learning model to determinewhich view of the FMX an image corresponds to. The system 400 may beused to train a machine learning model to classify the view an imagerepresents for use in pre-processing an image at step 108 of the method100.

In dentistry, an FMX is often taken to gain comprehensive imagery oforal anatomy. Standard views are categorized by the anatomic regionsequence indicating the anatomic region being viewed such as jaw region,maxilla, or mandible and an anatomic region modifier sequence indicatinga particular sub-region being viewed such as premolar 2, molar 3,anterior 1, anterior 2, and anterior 3. In addition, each anatomicregion sequence and anatomic region sequence modifier has a lateralityindicting which side of the patient is being visualized, such as left(L), right (R), or ambiguous (A). Correct identification, diagnosis, andtreatment of oral anatomy and pathology rely on accurate pairing of FMXmounting information of each image.

In some embodiment, the system 400 may be used to train a machinelearning model to estimate the view of an image. Accordingly, the outputof the machine learning model for a given input image will be a viewlabel indicating an anatomic region sequence, anatomic region sequencemodifier, and laterality visualized by the image. In some embodiments,the CNN architecture may include an encoder-based residually connectedCNN with attention-gated skip connections and deep-supervision asdescribed below.

In the system 400, A training algorithm 402 takes as inputs trainingdata entries that each include an image 404 according to any of theimaging modalities described herein and a view label 406 indicatingwhich of the view the image corresponds to (anatomic region sequence,anatomic region sequence modifier, and laterality). The view label 406for an image may be assigned by a human observing the image anddetermining which of the image views it is. For example, a licenseddentist may determine the label 406 for each image 404.

The training algorithm 402 may operate with respect to a loss function408 and modify a machine learning model 410 in order to reduce the lossfunction 408 of the model 410. In this case, the loss function 408 maybe a function that is zero when a view label output by the model 410 foran image 406 matches the view label 406 for that image 404 and isnon-zero, e.g. 1, when the view label output does not match the viewlabel 406. Inasmuch as there are three parts to each label (anatomicregion sequence, anatomic region modifier sequence, and laterality)there may be three loss functions 408, one for each part that is zerowhen the estimate for that part is correct and non-zero, e.g. 1, whenthe estimate for that part is incorrect. Alternatively, the lossfunction 408 may output a single value decreases with the number ofparts of the label that are correct and increase with the number ofparts of the label that are incorrect

The training algorithm 402 may train a machine learning model 410embodied as a CNN. In the illustrated embodiment, the CNN includes sevenmulti-scale stages 312 followed by a fully connected layer 414 thatoutputs an estimate for the anatomic region sequence, anatomic regionmodifier sequence, and laterality of an input image 404. Eachmulti-scale stage 412 may contain three 3×3 convolutional layers thatmay be paired with batchnormalization and leaky rectified linear units(LeakyReLU). The first and last convolutional layers of a stage 412 maybe concatenated via residual connections which help reduce redundancywithin the network by propagating shallow information to deeper parts ofthe network.

Each multi-scale stage 412 may be downscaled by a factor of two at theend of each multi-scale stage 412, such as by max pooling. The third andfifth multi-scale stages 412 may be passed through attention gates 418a, 418 b, respectively, before being concatenated with the last stage412. For example, the gating signal of attention gate 418 a that isapplied to the output of the third stage 412 may be derived from thefifth stage 412 and the gating signal applied by attention gate 418 b tothe output of the fifth stage 412 may be derived from the seventh stage412. Not all regions of the image are relevant for classification, soattention gates 418 a, 418 b may be used to selectively propagatesemantically meaningful information to deeper parts of the network.

The input images 404 may be raw 128×128 images, which may be rotated toa standard orientation according to the approach of FIG. 3. The output416 of the machine learning model 410 may be a likelihood score for eachof the anatomic region sequence, anatomic region modifier sequence, andlaterality of the input image 404. The loss function 408 may be trainedwith categorical cross entropy, which considers each part of a label(anatomic region sequence, anatomic region modifier sequence, andlaterality) to be an orthogonal category. Adam optimization may be usedduring training, which automatically estimates the lower order momentsand helps estimate the step size which desensitizes the training routineto the initial learning rate.

In at least one possible embodiment, the images 404 are 3D images, suchas a CT scan. Accordingly, the 3×3 convolutional kernels of themulti-scale stages 412 may be replaced with 3×3×3 convolutional kernels.The output of the machine learning model 410 in such embodiments may bea mapping of the CT scan to one of a number of regions within the oralcavity, such as the upper right quadrant, upper left quadrant, lowerleft quadrant, and lower right quadrant.

The training algorithm 402 and utilization of the trained machinelearning model 410 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

FIG. 5 is a schematic block diagram of a system 500 for removing imagecontamination in accordance with an embodiment of the present invention.The system 500 may be used to train a machine learning model to removecontamination from images for use in pre-processing an image at step 108of the method 100. In some embodiment, contamination may be removed froman image using the approach of FIG. 5 to obtain a corrected image andthe corrected image may then be reoriented using the approach of FIG. 3to obtain a reoriented image (though the image output from the approachof FIG. 3 may not always be rotated relative to the input image). Thereoriented image may then be used to classifying the FMX view of theimage using the approach of FIG. 4.

In some embodiment, the system 500 may be used to train a machinelearning model to output an improved quality image for a given inputimage. In order to establish the correct diagnosis from dental images,it is often useful to have high resolution, high contrast, and artifactfree images. It can be difficult to properly delineate dental anatomy ifimage degradation has occurred due to improper image acquisition, faultyhardware, patient setup error, or inadequate software. Poor imagequality can take many forms such as noise contamination, poor contrast,or low resolution. The illustrated system 500 may be used to solve thisproblem.

In the system 500, A training algorithm 502 takes as inputs contaminatedimages 504 and real images 506. As for other embodiments, the images504, 506 may be according to any of the imaging modalities describedherein. The images 504 and 506 are unpaired in some embodiments, meaningthe real images 506 are not uncontaminated versions of the contaminatedimages 504. Instead, the real images 506 may be selected from arepository of images and used to assess the realism of synthetic imagesgenerated using the system 500. The contaminated images 504 may beobtained by adding contamination to real images in the form of noise,distortion, or other defects. The training algorithm 502 may operatewith respect to one or more loss functions 508 and modify a machinelearning model 510 in order to reduce the loss functions 508 of themodel 510.

In the illustrated embodiment, the machine learning model 510 may beembodied as a generative adversarial network (GAN) including a generator512 and a discriminator 514. The generator 512 may be embodied as anencoder-decoder generator including seven multi-scale stages 516 in theencoder and seven multi-scale stages 518 in the decoder (the last stage516 of the encoder being the first stage of the decoder). Thediscriminator 514 may include five multi-scale stages 522.

Each multi-scale stage 516, 518 within the generator 512 may use 4×4convolutions paired with batchnormalization and rectified linear unit(ReLU) activations. Convolutional downsampling may be used to downsampleeach multi-scale stage 516 and transpose convolutions may be usedbetween the multi-scale stages 518 to incrementally restore the originalresolution of the input signal. The resulting high-resolution outputchannels of the generator 512 may be passed through a 1×1 convolutionallayer and hyperbolic tangent activation function to produce a syntheticimage 520. At each iteration, the synthetic image 520 and a real image506 from a repository of images may be passed through the discriminator514.

The discriminator 514 produces as an output 524 a realism matrix that isan attempt to differentiate between real and fake images. The realismmatrix is a matrix of values, each value being an estimate as to whichof the two input images is real. The loss function 508 may then operateon an aggregation of the values in the realism matrix, e.g. average ofthe values, a most frequently occurring value of the values, or someother function. The closer the aggregation is to the correct conclusion(determining that the synthetic image 520 is fake), the lower the outputof the loss function 508. The realism matrix may be preferred over aconventional single output signal discriminator because it is bettersuited to capture local image style characteristics and it is easier totrain.

In some embodiments, the loss functions 508 utilize level 1 (L1) loss tohelp maintain the spatial congruence of the synthetic image 520 and realimage 506 and adversarial loss to encourage realism. The generator 512and discriminator 514 may be trained simultaneously until thediscriminator 514 can no longer differentiate between synthetic and realimages or a Nash equilibrium has been reached.

In at least one possible embodiment, the system 500 may operate onthree-dimensional images 504, 506, such as a CT scan. This may includereplacing the 4×4 convolutional kernels with 4×4×4 convolutional kernelsand replacing the 1×1 convolutional kernels with 1×1×1 convolutionalkernels.

The training algorithm 502 and utilization of the trained machinelearning model 510 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

FIG. 6A is a schematic block diagram of system 600 for performing imagedomain transfer in accordance with an embodiment of the presentinvention. FIG. 6B is a schematic block diagram of cyclic GAN for usewith the system 600.

The system 600 may be used to train a machine learning model 610, e.g. acyclic GAN, to transform an image obtained using one image modality toan image from another image modality. Examples of transforming betweentwo-dimensional imaging modalities may include transforming between anytwo of the following: an X-ray, CBCT image, a slice of a CT scan, anintra-oral photograph, cephalometric, panoramic, or othertwo-dimensional imaging modality. In some embodiments, the machinelearning model 610 may transform between any two of the followingthree-dimensional imaging modalities, such as a CT scan, magneticresonance imaging (MM) image, a three-dimensional optical image, LIDAR(light detection and ranging) point cloud, or other three-dimensionalimaging modality. In some embodiments, the machine learning model 610may be trained to transform between any one of the two-dimensionalimaging modalities and any one of the three-dimensional imagingmodalities. In some embodiments, the machine learning model 610 may betrained to transform between any one of the three-dimensional imagingmodalities and any one of the two-dimensional imaging modalities.

In some embodiments, the machine learning model 610 may be trained totranslate between a first imaging modality that is subject to distortion(e.g., foreshortening or other type of optical distortion and a secondimaging modality that is less subject to distortion. Deciphering dentalpathologies on an image may be facilitated by establishing absolutemeasurements between anatomical landmarks (e.g., in a standard units ofmeasurement, such as mm). Two-dimensional dental images interpret athree-dimensional space by estimating x-ray attenuation along a pathfrom the target of an x-ray source to a photosensitive area of film ordetector array. The relative size and corresponding lengths of anyintercepting anatomy will be skewed as a function of their positionrelative to the x-ray source and imager. Furthermore, intra-oral opticaldental images capture visual content by passively allowing scatteredlight to intercept a photosensitive detector array. Objects locatedfurther away from the detector array will appear smaller than closerobjects, which makes estimating absolute distances difficult. Correctingfor spatial distortion and image contamination can make decipheringdental pathologies and anatomy on x-ray, optical, or CBCT images moreaccurate. The machine learning model 610 may therefore be trained totranslate between a distorted source domain and an undistorted targetdomain using unpaired dental images.

The transformation using the machine learning model 610 may be performedon an image that has been reoriented using the approach of FIG. 3 and/orhad contamination removed using the approach of FIG. 5. Transformationusing the machine learning model 610 may be performed to obtain atransformed image and the transformed image may then be used forsubsequent processing according to some or all of steps 110, 112, and114 of the method 100. Transformation using the machine learning model610 may be performed as part of the preprocessing of step 108 of themethod 100.

In the system 600, A training algorithm 602 takes as inputs images 604from a source domain (first imaging modality, e.g., a distorted imagedomain) and images 606 from a target domain (second imaging modality,e.g., a non-distorted image domain or domain that is less distorted thanthe first domain). The images 604 and 606 are unpaired in someembodiments, meaning the images 606 are not transformed versions of theimages 504 or paired such that an image 604 has a corresponding image606 visualizing the same patient's anatomy. Instead, the images 506 maybe selected from a repository of images and used to assess thetransformation of the images 604 using the machine learning model 610.The training algorithm 502 may operate with respect to one or more lossfunctions 608 and modify a machine learning model 610 in order to reducethe loss functions 608 of the model 610.

FIG. 6B illustrates the machine learning model 610 embodied as a cyclicGAN, such as a densely-connected cycle consistent cyclic GAN (D-GAN).The cyclic GAN may include a generator 612 paired with a discriminator614 and a second generator 618 paired with a second discriminator 620.The generators 612, 618 may be implemented using any of the approachesdescribed above with respect to the generator 512. Likewise, thediscriminators 614, 620 may be implemented using any of the approachesdescribed above with respect to the discriminator 514.

Training of the machine learning model 610 may be performed by thetraining algorithm 602 as follows:

(Step 1) An image 604 in the source domain is input to generator 612 toobtain a synthetic image 622 in the target domain.

(Step 2) The synthetic image 622 and an unpaired image 606 from thetarget domain are input to the discriminator 614, which produces arealism matrix output 616 that is the discriminator's estimate as towhich of the images 622, 606 is real.

(Step 3) Loss functions LF1 and LF2 are evaluated. Loss function LF1 islow when the output 616 indicates that the synthetic image 622 is realand that the target domain image 606 is fake. Since the output 616 is amatrix, the loss function LF1 may be a function of the multiple values(average, most frequently occurring value, etc.). Loss function LF2 islow when the output 616 indicates that the synthetic image 622 is fakeand that the target domain image 606 is real. Thus, the generator 612 istrained to “fool” the discriminator 614 and the discriminator 614 istrained to detect fake images. The generator 612 and discriminator 614may be trained concurrently.

(Step 4) The synthetic image 622 is input to the generator 618. Thegenerator 618 transforms the synthetic image 622 into a synthetic sourcedomain image 624.

(Step 5) A loss function LF3 is evaluated according to a comparison ofthe synthetic source domain image 624 and the source domain image 604that was input to the generator 612 at Step 1. The loss function LF3decreases with similarity of the images 604, 622.

(Step 6) A real target domain image 606 (which may be the same as ordifferent from that input to the discriminator 614 at Step 2, is inputto the generator 618 to obtain another synthetic source domain image624. This synthetic source domain image 624 is input to thediscriminator 620 along with a source domain image 604, which may be thesame as or different from the source domain image 604 input to thegenerator 612 at Step 1.

(Step 7) The output 626 of the discriminator 620, which may be a realismmatrix, is evaluated with respect to a loss function LF4 and a lossfunction LF5. Loss function LF4 is low when the output 626 indicatesthat the synthetic image 624 is real and that the source domain image604 is fake. Since the output 626 is a matrix, the loss function LF4 maybe a function of the multiple values (average, most frequently occurringvalue, etc.). Loss function LF5 is low when the output 626 indicatesthat the synthetic image 624 is fake and that the source domain image604 is real.

(Step 8) The synthetic image 624 obtained at Step 6 is input to thegenerator 612 to obtain another synthetic target domain image 622.

(Step 9) A loss function LF6 is evaluated according to a comparison ofthe synthetic target domain image 622 from Step 8 and the target domainimage 606 that was input to the generator 618 at Step 6. The lossfunction LF6 decreases with similarity of the images 606, 622.

(Step 10) Model parameters of the generators 612, 618 and thediscriminators 614, 620 are tuned according to the outputs of the lossfunctions LF1, LF2, LF3, LF4, LF5, LF6, and LF7.

Steps 1 through 10 may be repeated until an ending condition is reached,such as when the discriminators 616, 620 can no longer distinguishbetween synthetic and real images (e.g., only correct 50 percent of thetime), a Nash equilibrium is reached, or some other ending condition isreached.

Since the machine learning model 610 trains on un-paired images, aconventional L1 loss may be inadequate because the source and targetdomains are not spatially aligned. To promote spatial congruence betweenthe source input image 604 and synthetic target image 622, theillustrated reverse GAN network (generator 618 and discriminator 620)may be used in combination with the illustrated forward GAN network(generator 612 and discriminator 614). Spatial congruence is thereforeencouraged by evaluating L1 loss (loss function LF3) at Step 5 andevaluating L1 loss (loss function LF6) at Step 9.

Once training is ended, the generator 612 may be used to transform aninput image in the source domain to obtain a transformed image in thetarget domain. The discriminators 616, 620 and the second generator 618may be ignored or discarded during utilization.

The training algorithm 602 and utilization of the trained machinelearning model 610 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 600 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 4×4 and 1×1) with three-dimensionalconvolution kernels (e.g., 4×4×4 or 1×1×1).

FIG. 7 is a schematic block diagram of system 700 for labeling teeth inaccordance with an embodiment of the present invention. In order toestablish the correct diagnosis and treatment protocol from dentalimages, it is often useful to first identify tooth labels. It can bechallenging to correctly label teeth on abnormal anatomy because teethmight have caries, restorations, implants, or other characteristics thatmight hamper tooth identification. Furthermore, teeth might migrate andcause gaps between adjacent teeth or move to occupy gaps that resultedfrom extractions. The illustrated system 700 may utilizes adversarialloss and individual tooth level loss to label teeth in an image.

In the system 700, A training algorithm 702 takes as inputs trainingdata entries that each include an image 704 and labels 706 a for teethrepresented in that image. For example, the labels 706 a may be a toothlabel mask in which pixel positions of the image 704 that correspond toa tooth are labeled as such, e.g. with the tooth number of a labeledtooth. The labels 706 a for an image may be generated by a licenseddentist. The training algorithm 702 may further make use of unpairedlabels 706 b, i.e., pixels masks for images of real teeth, such as mightbe generated by a licensed dentist that do not correspond to the images704 or labels 706 a.

The training algorithm 702 may operate with respect to one or more lossfunctions 708 and modify a machine learning model 710 in order to trainthe machine learning model 710 to label teeth in a given input image.The labeling performed using the machine learning model 710 may beperformed on an image that has been reoriented using the approach ofFIG. 3 and had contamination removed using the approach of FIG. 5. Insome embodiments, a machine learning model 710 may be trained for eachview of the FMX such that the machine learning model 710 is used tolabel teeth in an image that has previously been classified using theapproach of FIG. 4 as belonging to the FMX view for which the machinelearning model 710 was trained.

In the illustrated embodiment, the machine learning model 710 includes aGAN including a generator 712 and a discriminator 714. The discriminator714 may have an output 716 embodied as a realism matrix that may beimplemented as for other realism matrices in other embodiments asdescribed above. The output of the generator 712 may also be input to aclassifier 718 trained to produce an output 720 embodied as a toothlabel, e.g. pixel mask labeling a portion of an input image estimated toinclude a tooth.

As for other GAN disclosed herein, the generator 712 may include sevenmulti-scale stage deep encoder-decoder generator, such as using theapproach described above with respect to the generator 512. For themachine learning model 710, the output channels of the generator 712 maybe passed through a 1×1 convolutional layer as for the generator 512.However, the 1×1 convolution layer may further include a sigmoidalactivation function to produce tooth labels. The generator 712 maylikewise have stages of a different size than the generator 512, e.g.,an input stage of 256×256 with downsampling by a factor of two betweenstages.

The discriminator 714 may be implemented using the approach describedabove for the discriminator 514. However, in the illustrated embodiment,the discriminator 514 includes four layers, though five layers as forthe discriminator 514 may also be used.

The classifier 718 may be embodied as an encoder including sixmulti-scale stages 722 coupled to a fully connected layer 724, theoutput 720 of the fully connected layer 314 being a tooth label mask. Insome embodiments, each multi-scale stage 722 may contain three 3×3convolutional layers, which may be paired with batch-normalization andleaky rectified linear units (LeakyReLU). The first and lastconvolutional layers of each stage 722 may be concatenated via denseconnections which help reduce redundancy within the CNN by propagatingshallow information to deeper parts of the CNN. Each multi-scale networkstage 722 may be downscaled by a factor of two at the end of eachmulti-scale stage 722 by convolutional downsampling.

Training of the machine learning model 710 may be performed by thetraining algorithm 702 according to the following method:

(Step 1) An image 704 is input to the generator 712, which outputssynthetic labels 726 for the teeth in the image 704. The syntheticlabels 726 and unpaired tooth labels 706 b from a repository are inputto the discriminator 714. The discriminator 714 outputs a realism matrixwith each value in the matrix being an estimate as to which of the inputlabels 726, 706 b is real.

(Step 2) Input data 728 is input to the classifier 718, the input data728 including layers including the original image 704 concatenated withthe synthetic label 726 from Step 1. In response, the classifier 718outputs its own synthetic label on its output 720.

(Step 3) The loss functions 708 are evaluated. This may include a lossfunction LF1 based on the realism matrix output at Step 1 such that theoutput of LF1 decreases with increase in the number of values of therealism matrix that indicate that the synthetic labels 726 are real.Step 3 may also include evaluating a loss function LF2 based on therealism matrix such that the output of LF2 decreases with increase inthe number of values of the realism matrix that indicate that thesynthetic labels 726 are fake. Step 3 may include evaluating a lossfunction LF3 based on a comparison of the synthetic label output by theclassifier 718 and the tooth label 706 a paired with the image 704processed at Step 1. In particular, the output of the loss function LF3may decrease with increasing similarity of the synthetic label outputfrom the classifier 718 and the tooth label 706 a.

(Step 4) The training algorithm 702 may use the output of loss functionLF1 to tune parameters of the generator 712, the output of loss functionLF2 to tune parameters of the discriminator 714, and the output of theloss function LF3 to tune parameters of the classifier 718. In someembodiments, the loss functions 708 are implemented as an objectivefunction that utilizes a combination of softdice loss between thesynthetic tooth label 726 and the paired truth tooth label 706 a,adversarial loss from the discriminator 714, and categorical crossentropy loss from the classifier 718.

Steps 1 through 4 may be repeated such that the generator 712,discriminator 714, and classifier 718 are trained simultaneously. Steps1 through 4 may continue to be repeated until an end condition isreached, such as until loss function LF3 meets a minimum value or otherending condition and LF2 is such that the discriminator 714 identifiesthe synthetic labels 726 as real 50 percent of the time or Nashequilibrium is reached.

During utilization, the discriminator 716 may be ignored or discarded.Images may then be processed by the generator 712 to obtain a syntheticlabel 726, which is then concatenated with the image to obtain data 728,which is then processed by the classifier 718 to obtain one or moretooth labels.

The training algorithm 702 and utilization of the trained machinelearning model 710 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 700 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 4×4 and 1×1) with three-dimensionalconvolution kernels (e.g., 4×4×4 or 1×1×1).

FIG. 8 is a schematic block diagram of system 800 for labeling featuresof teeth and surrounding areas in accordance with an embodiment of thepresent invention. For example, the system 800 may be used to labelanatomical features such as the cementum enamel junction (CEJ), bonypoints on the maxilla or mandible that are relevant to the diagnosis ofperiodontal disease, gingival margin, junctional epithelium, or otheranatomical feature.

In the system 800, A training algorithm 802 takes as inputs trainingdata entries that each include an image 804 a and labels 804 b for teethrepresented in that image, e.g., pixel masks indicating portions of theimage 804 a corresponding to teeth. The labels 804 b for an image 804 amay be generated by a licensed dentist or automatically generated usingthe tooth labeling system 700 of FIG. 7. Each training data entry mayfurther include a feature label 806 that may be embodied as a pixel maskindicating pixels in the image 804 a that correspond to an anatomicalfeature of interest. The image 804 a may be an image that has beenreoriented according to the approach of FIG. 3 and/or has hadcontamination removed using the approach of FIG. 4. In some embodiments,a machine learning model 810 may be trained for each view of the FMXsuch that the machine learning model 810 is used to label teeth in animage that has previously been classified using the approach of FIG. 4as belonging to the FMX view for which the machine learning model 810was trained.

As described below, two versions of the feature label 806 may be used.An non-dilated version is used in which only pixels identified ascorresponding to the anatomical feature of interest are labeled. Adilated version is also used in which the pixels identified ascorresponding to the anatomical feature of interest are dilated: a maskis generated that includes a probability distribution for each pixelrather than binary labels. Pixels that were labeled in the non-dilatedversion will have the highest probability values, but adjacent pixelswill have probability values that decay with distance from the labeledpixels. The rate of decay may be according to a gaussian function orother distribution function. Dilation facilitates training of a machinelearning model 810 since a loss function 808 will increase graduallywith distance of inferred pixel locations from labeled pixel locationsrather than being zero at the labeled pixel locations and the samenon-zero value at every other pixel location.

The training algorithm 802 may operate with respect to one or more lossfunctions 808 and modify a machine learning model 810 in order to trainthe machine learning model 810 to label the anatomical feature ofinterest in a given input image. The labeling performed using themachine learning model 810 may be performed on an image that has beenreoriented using the approach of FIG. 3 and had contamination removedusing the approach of FIG. 5. In some embodiments, a machine learningmodel 810 may be trained for each view of the FMX such that the machinelearning model 810 is used to label teeth in an image that haspreviously been classified using the approach of FIG. 4 as belonging tothe FMX view for which the machine learning model 710 was trained. Asnoted above, the tooth labels 804 b may be generated using the labelingapproach of FIG. 8.

In the illustrated embodiment, the machine learning model 810 includes aGAN including a generator 812 and a discriminator 814. The discriminator814 may have an output 816 embodied as a realism matrix that may beimplemented as for other realism matrices in other embodiments asdescribed above. The output of the generator 812 may also be input to aclassifier 818 trained to produce an output 820 embodied as a label ofthe anatomical feature of interest, e.g. pixel mask labeling a portionof an input image estimated to correspond to the anatomical feature ofinterest. The generator 812 and discriminator 814 may be implementedaccording to the approach described above for the generator 712 anddiscriminator 714. The classifier 818 may be implemented according tothe approach described above for the classifier 718.

Training of the machine learning model 810 may be performed by thetraining algorithm 802 as follows:

(Step 1). The image 804 a and tooth label 804 b are concatenated andinput to the generator 812. Concatenation in this and other systemsdisclosed herein may include inputting two images (e.g., the image 804 aand tooth label 804 b) as different layers to the generator 812, such asin the same manner that different color values (red, green, blue) of acolor image may be processed by a CNN according to any approach known inthe art. The generator 812 may output synthetic labels 822 (e.g., pixelmask) of the anatomical feature of interest based on the image 804 a andtooth label 804 b.

(Step 2) The synthetic labels 822 and real labels 824 (e.g., anindividual pixel mask from a repository including one or more labels)are then input to the discriminator 814. The real labels 824 areobtained by labeling the anatomical feature of interest in an image thatis not paired with the image 804 a from Step 1. The discriminator 814produces a realism matrix at its output 816 with each value of thematrix indicating whether the synthetic label 822 is real or fake. Insome embodiments, the real labels 824 may be real labels that have beendilated using the same approach used to dilate the feature labels 806 toobtain the dilated feature labels 806. In this manner, the generator 812may be trained to generate dilated synthetic labels 822.

(Step 3) The image 804 a, tooth label 804 b, and synthetic labels 822are concatenated to obtain a concatenated input 826, which is then inputto the classifier 818. The classifier 818 processes the concatenatedinput 826 and produces output labels 828 (pixel mask) that is anestimate of the pixels in the image 804 a that correspond to theanatomical feature of interest.

(Step 4) The loss functions 808 are evaluated with respect to theoutputs of the generator 812, discriminator 814, and classifier 818.This may include evaluating a loss function LF1 based on the realismmatrix output by the discriminator 814 at Step 2 such that the output ofLF1 decreases with increase in the number of values of the realismmatrix that indicate that the synthetic labels 822 are real. Step 4 mayalso include evaluating a loss function LF2 based on the realism matrixsuch that the output of LF2 decreases with increase in the number ofvalues of the realism matrix that indicate that the synthetic labels 822are fake. Step 4 may include evaluating a loss function LF3 based on acomparison of the synthetic label 822 output by the generator 812 andthe dilated tooth feature label 806. In particular, the output of theloss function LF3 may decrease with increasing similarity of thesynthetic label 822 and the dilated tooth label 804 b. Step 4 mayinclude evaluating a loss function LF4 based on a comparison of thesynthetic labels 828 to the non-dilated tooth label 804 b such that theoutput of the loss function LF4 decreases with increasing similarity ofthe synthetic labels 828 and the non-dilated tooth label 804 b.

(Step 5) The training algorithm 802 may use the output of loss functionLF1 and LF3 to tune parameters of the generator 812. In particular, thegenerator 812 may be tuned to both generate realistic labels accordingto LF1 and to generate a probability distribution of a dilated toothlabel according to LF3. The training algorithm 802 may use the output ofloss function LF2 to tune parameters of the discriminator 814 and theoutput of the loss function LF4 to tune parameters of the classifier818.

Steps 1 through 5 may be repeated such that the generator 812,discriminator 814, and classifier 818 are trained simultaneously. Steps1 through 5 may continue to be repeated until an end condition isreached, such as until loss functions LF1, LF3, and LF4 meet a minimumvalue or other ending condition, which may include the discriminator 714identifying the synthetic label 822 as real 50 percent of the time orNash equilibrium is reached.

The training algorithm 802 and utilization of the trained machinelearning model 810 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 800 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 4×4 and 1×1) with three-dimensionalconvolution kernels (e.g., 4×4×4 or 1×1×1).

During utilization to identify the anatomical feature of interest, thediscriminator 814 may be ignored or discarded. Input images 804 a withtooth labels 804 b but without feature labels 806 are processed usingthe discriminator to obtain a synthetic labels 822.

The image 804 a, tooth labels 804 b, and synthetic labels 822 areconcatenated and input to the classifier 818 that outputs a label 828that is an estimate of the pixels corresponding to the anatomicalfeature of interest.

Below are example applications of the system 800 to label anatomicalfeatures:

-   -   In order to establish the correct diagnosis from dental images,        it is often useful to identify the cementum enamel junction        (CEJ). The CEJ can be difficult to identify in dental X-ray,        CBCT, and intra-oral images because the enamel is not always        clearly differentiated from dentin and the CEJ might be        obfuscated by overlapping anatomy from adjacent teeth or        improper patient setup and image acquisition geometry. To solve        this problem, the system 800 may be used to identify the CEJ        from images as the anatomical feature of interest.    -   In order to establish the correct diagnosis from dental images,        it is often useful to identify the point on maxilla or mandible        that correspond the periodontal disease. These boney points can        be difficult to identify in dental x-ray, CBCT, and intra-oral        images because the boney point is not always clearly        differentiated from other parts of the bone and might be        obfuscated by overlapping anatomy from adjacent teeth or        improper patient setup and image acquisition geometry. To solve        this problem, the system 800 may be used to identify the boney        point as the anatomical feature of interest.    -   In order to establish the correct diagnosis from dental images,        it is often useful to identify the gingival margin. This soft        tissue point can be difficult to identify in dental X-ray, CBCT,        and intra-oral images because the soft tissue point is not        always clearly differentiated from other parts of the image and        might be obfuscated by overlapping anatomy from adjacent teeth        or improper patient setup and image acquisition geometry. To        solve this problem, the system 800 may be used to identify the        gingival margin as the anatomical feature of interest.    -   In order to establish the correct diagnosis from dental images,        it is often useful to identify the junctional Epithelium (JM).        This soft tissue point can be difficult to identify in dental        X-ray, CBCT, and intra-oral images because the soft tissue point        is not always clearly differentiated from other parts of the        image and might be obfuscated by overlapping anatomy from        adjacent teeth or improper patient setup and image acquisition        geometry. To solve this problem, the system 800 may be used to        identify the JE as the anatomical feature of interest.

FIG. 9 is a schematic block diagram of system 900 for determiningclinical attachment level (CAL) in accordance with an embodiment of thepresent invention. In order to establish the correct periodontaldiagnosis from dental images, it is often useful to identify theclinical attachment level (CAL). CAL can be difficult to identify indental x-ray, CBCT, and intra-oral images because CAL relates to thecementum enamel junction (CEJ), probing depth, junctional epithelium(JE), and boney point (B) on the maxilla or mandible which might notalways be visible. Furthermore, the contrast of soft tissue anatomy canbe washed out from adjacent boney anatomy because bone attenuates morex-rays than soft tissue. Also, boney anatomy might not always bedifferentiated from other parts of the image or might be obfuscated byoverlapping anatomy from adjacent teeth or improper patient setup andimage acquisition geometry. The illustrated system 900 may therefore beused to determine CAL.

In the system 900, A training algorithm 802 takes as inputs trainingdata entries that each include an image 904 a and labels 904 b, e.g.,pixel masks indicating portions of the image 904 a corresponding toteeth, CEJ, JE, B, or other anatomical features. The labels 904 b for animage 904 a may be generated by a licensed dentist or automaticallygenerated using the tooth labeling system 700 of FIG. 7 and/or thelabeling system 800 of FIG. 8. The image 904 a may have been one or bothof reoriented according to the approach of FIG. 3 decontaminatedaccording to the approach of FIG. 5. In some embodiments, a machinelearning model 910 may be trained for each view of the FMX such that themachine learning model 910 is used to label teeth in an image that haspreviously been classified using the approach of FIG. 4 as belonging tothe FMX view for which the machine learning model 910 was trained.

Each training data entry may further include a CAL label 906 that may beembodied as a numerical value indicating the CAL for a tooth, or eachtooth of a plurality of teeth, represented in the image. The CAL label906 may be assigned to the tooth or teeth of the image by a licenseddentist.

The training algorithm 902 may operate with respect to one or more lossfunctions 908 and modify a machine learning model 910 in order to trainthe machine learning model 910 to determine one or more CAL values forone or more teeth represented in an input image.

In the illustrated embodiment, the machine learning model 910 is a CNNincluding seven multi-scale stages 912 followed by a fully connectedlayer 914 that outputs a CAL estimate 916, such as a CAL estimate 916for each tooth identified in the labels 904 b. Each multi-scale stage912 may contain three 3×3 convolutional layers, paired withbatchnormalization and leaky rectified linear units (LeakyReLU). Thefirst and last convolutional layers of each stage 912 may beconcatenated via dense connections which help reduce redundancy withinthe network by propagating shallow information to deeper parts of thenetwork. Each multi-scale stage 912 may be downscaled by a factor of twoat the end of each multi-scale stage by convolutional downsampling withstride 2. The third and fifth multi-scale stages 912 may be passedthrough attention gates 918 a, 918 b before being concatenated with thelast multi-scale stage 912. The attention gate 918 a applied to thethird stage 912 may be gated by a gating signal derived from the fifthstage 912. The attention gate 918 b applied to the fifth stage 912 maybe gated by a gating signal derived from the seventh stage 912. Not allregions of the image are relevant for estimating CAL, so attention gates918 a, 918 b may be used to selectively propagate semanticallymeaningful information to deeper parts of the network. Adam optimizationmay be used during training which automatically estimates the lowerorder moments and helps estimate the step size which desensitizes thetraining routine to the initial learning rate.

A training cycle of the training algorithm 902 may include concatenatingthe image 904 a with the labels 904 b of a training data entry andprocessing the concatenated data with the machine learning model 910 toobtain a CAL estimate 916. The CAL estimate 916 is compared to the CALlabel 906 using the loss function 908 to obtain an output, such that theoutput of the loss function decreases with increasing similarity betweenthe CAL estimate 916 and the CAL label 906. The training algorithm 902may then adjust the parameters of the machine learning model 910according to the output of the loss function 908. Training cycles may berepeated until an ending condition is reached, such as the loss function908 reaching a minimum value or other ending condition being achieved.

The training algorithm 902 and utilization of the trained machinelearning model 810 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 900 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 3×3 and 1×1) with three-dimensionalconvolution kernels (e.g., 3×3×3 or 1×1×1).

FIG. 10 is a system 1000 for determining pocket depth (PD) in accordancewith an embodiment of the present invention. In order to establish thecorrect periodontal diagnosis from dental images, it is often useful toidentify the pocket depth (PD). PD can be difficult to identify indental X-ray, CBCT, and intra-oral images because PD relates to thecementum enamel junction (CEJ), junctional epithelium (JE), gingivalmargin (GM), and boney point (B) on the maxilla or mandible which mightnot always be visible. Furthermore, the contrast of soft tissue anatomycan be washed out from adjacent boney anatomy because bone attenuatesmore x-rays than soft tissue. Also, boney anatomy might not always bedifferentiated from other parts of the image or might be obfuscated byoverlapping anatomy from adjacent teeth or improper patient setup andimage acquisition geometry. The illustrated system 1000 may therefore beused to determine PD.

In the system 1000, a training algorithm 1002 takes as inputs trainingdata entries that each include an image 1004 a and labels 1004 b, e.g.,pixel masks indicating portions of the image 1004 a corresponding toteeth, GM, CEJ, JE, B, or other anatomical features. The labels 1004 bfor an image 1004 a may be generated by a licensed dentist orautomatically generated using the tooth labeling system 700 of FIG. 7and/or the labeling system 800 of FIG. 8. Each training data entry mayfurther include a PD label 1006 that may be embodied as a numericalvalue indicating the pocket depth for a tooth, or each tooth of aplurality of teeth, represented in the image. The PD label 1006 may beassigned to the tooth or teeth of the image by a licensed dentist.

The image 1004 a may have been one or both of reoriented according tothe approach of FIG. 3 decontaminated according to the approach of FIG.5. In some embodiments, a machine learning model 1010 may be trained foreach view of the FMX such that the machine learning model 1010 is usedto label teeth in an image that has previously been classified using theapproach of FIG. 4 as belonging to the FMX view for which the machinelearning model 1010 was trained.

The training algorithm 1002 may operate with respect to one or more lossfunctions 1008 and modify a machine learning model 1010 in order totrain the machine learning model 1010 to determine one or more PD valuesfor one or more teeth represented in an input image. In the illustratedembodiment, the machine learning model 1010 is a CNN that may beconfigured as described above with respect to the machine learning model910.

A training cycle of the training algorithm 1002 may includeconcatenating the image 1004 a with the labels 1004 b of a training dataentry and processing the concatenated data with the machine learningmodel 1010 to obtain a PD estimate 1016. The PD estimate 1016 iscompared to the PD label 1006 using the loss function 1008 to obtain anoutput, such that the output of the loss function decreases withincreasing similarity between the PD estimate 1016 and the PD label1006. The training algorithm 1002 may then adjust the parameters of themachine learning model 1010 according to the output of the loss function1008. Training cycles may be repeated until an ending condition isreached, such as the loss function 1008 reaching a minimum value orother ending condition being achieved.

The training algorithm 1002 and utilization of the trained machinelearning model 1010 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 1000 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 3×3 and 1×1) with three-dimensionalconvolution kernels (e.g., 3×3×3 or 1×1×1).

FIG. 11 is a schematic block diagram of a system 1100 for determining aperiodontal diagnosis in accordance with an embodiment of the presentinvention. The system 1100 may be used as part of step 114 of the method100 in order to diagnose a condition that may trigger evaluation of adecision hierarchy. For example, if the machine learning model discussedbelow indicates that a diagnosis is appropriate, the condition of step116 of the method 100 may be deemed to be satisfied.

In order to assess the extent of periodontal disease it is often usefulto observe a multitude of dental images. Periodontal disease can bedifficult to diagnosis on dental X-rays, CBCTs, and intra-oral imagesbecause periodontal disease relates to the cementum enamel junction(CEJ), junctional epithelium (JE), gingival margin (GM), boney point (B)on the maxilla or mandible, pocket depth (PD), gingival health,comorbidities, and clinical attachment level (CAL), which might notalways be available. Furthermore, the contrast of soft tissue anatomycan be washed out from adjacent boney anatomy because bone attenuatesmore x-rays than soft tissue. Also, boney anatomy might not always bedifferentiated from other parts of the image or might be obfuscated byoverlapping anatomy from adjacent teeth or improper patient setup andimage acquisition geometry. To solve this problem, the illustratedsystem 1100 may be used in combination with the approaches of FIGS. 7through 10 in order to derive a comprehensive periodontal diagnosis. Thesystem 1100 may take advantage of an ensemble of unstructured imagingdata and structured data elements derived from tooth masks, CEJ points,GM points, JE information, bone level points. All of this informationmay be input into the system 1000 and non-linearly combined via amachine learning model 1110.

For compatibility, all structured information (e.g. pixel mask labels,PD, and CAL values obtained using the approaches of FIGS. 7 through 10)may be converted to binary matrices and concatenated with the rawimaging data used to derive the structured information into a singlen-dimensional array. Each image processed using the system 1100 may benormalized by the population mean and standard deviation of an imagerepository, such as a repository of images used for the unpaired imagesin the approach of FIGS. 5, 6A, 6B, 7, and 8 or some other repository ofimages.

In the system 1100, A training algorithm 1102 takes as inputs trainingdata entries that each include an image 1104 a and labels 1104 b, e.g.,pixel masks indicating portions of the image 1104 a corresponding toteeth, GM, CEJ, JE, B or other anatomical features. Each training dataentry may further include a diagnosis 1106, i.e. a periodontal diagnosisthat was determined by a licensed dentist to be appropriate for one ormore teeth represented in the image 1104 a.

The image 1104 a may be an image that has been oriented according to theapproach of FIG. 3 and had decontaminated according to the approach ofFIG. 4. In some embodiments, a machine learning model 1110 may betrained for each view of the FMX such that the machine learning model1110 is used to label teeth in an image that has previously beenclassified using the approach of FIG. 4 as belonging to the FMX view forwhich the machine learning model 1110 was trained.

The labels 1104 b for the image 1104 a of a training data entry may begenerated by a licensed dentist or automatically generated using thetooth labeling system 700 of FIG. 7 and/or the labeling system 800 ofFIG. 8. The labels 1104 b for a tooth represented in an image 1104 a mayfurther be labeled with a CAL value and/or a PD value, such asdetermined using the approaches of FIGS. 9 and 10 or by a licenseddentist. The CAL and/or PD labels may each be implemented as a pixelmask corresponding to the pixels representing a tooth and associatedwith the CAL value and PD value, respectively, determined for thattooth.

In some embodiments, other labels 1104 b may be used. For example, alabel 1104 b may label a tooth in an image with a pixel mask indicatinga past treatment with respect to that tooth. Other labels 1104 b mayindicate comorbidities of the patient represented in the image 1104 a.

The training algorithm 1102 may operate with respect to one or more lossfunctions 1108 and modify a machine learning model 1110 in order totrain the machine learning model 1110 to determine a predicted diagnosisfor one or more teeth represented in an input image.

In the illustrated embodiment, the machine learning model 1110 includesnine multi-scale stages 1112 followed by a fully connected layer 1114that outputs a predicted diagnosis 1116. Each multi-scale stage 1112 maycontain three 3×3 convolutional layers, paired with batchnormalizationand leaky rectified linear units (LeakyReLU). The first and lastconvolutional layers of each stage 1112 may be concatenated via denseconnections which help reduce redundancy within the network bypropagating shallow information to deeper parts of the network. Eachmulti-scale stage 1112 may be downscaled by a factor of two at the endof each multi-scale stage 1112, such as by convolutional downsamplingwith stride 2. The fifth and seventh multi-scale stages 1112 may bepassed through attention gates 1118 a, 1118 b before being concatenatedwith the last stage 1112. The attention gate 1118 a may be applied tothe fifth stage 1112 according to a gating signal derived from theseventh stage 1112. The attention gate 1118 b may be applied to theseventh stage 1112 according to a gating signal derived from the ninthstage 1112. Not all regions of the image are relevant for estimatingperiodontal diagnosis, so attention gates may be used to selectivelypropagate semantically meaningful information to deeper parts of thenetwork. Adam optimization may be used during training whichautomatically estimates the lower order moments and helps estimate thestep size which desensitizes the training routine to the initiallearning rate.

A training cycle of the training algorithm 1102 may includeconcatenating the image 1104 a with the labels 1104 b of a training dataentry and processing the concatenated data with the machine learningmodel 1110 to obtain a predicted diagnosis 1116. The predicted diagnosisis compared to the diagnosis 1106 using the loss function 1108 to obtainan output, such that the output of the loss function decreases withincreasing similarity between the diagnosis 1116 and the diagnosis 1106,which may simply be a binary value (zero of correct, non-zero if notcorrect). The training algorithm 1102 may then adjust the parameters ofthe machine learning model 1110 according to the output of the lossfunction 1108. Training cycles may be repeated until an ending conditionis reached, such as the loss function 1108 reaching a minimum value orother ending condition being achieved.

The training algorithm 1102 and utilization of the trained machinelearning model 1110 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 1100 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 3×3 and 1×1) with three-dimensionalconvolution kernels (e.g., 3×3×3 or 1×1×1).

In another variation, several outputs from multiple image modalities ormultiple images from a single modality are combined in an ensemble ofnetworks to form a comprehensive periodontal diagnosis or treatmentprotocol. For example, a system 1100 may be implemented for each imagingmodality of a plurality of imaging modalities. A plurality of images ofthe same patient anatomy according to the plurality of imagingmodalities may then be labeled and processed according to theircorresponding systems 1100. The diagnosis output for each imagingmodality may then be unified to obtain a combined diagnosis, such as byboosting, bagging, or other conventional machine learning methods suchas random forests, gradient boosting, or support vector machines (SVMs).

FIG. 12 is a schematic block diagram of a system 1200 for restoringmissing data to images in accordance with an embodiment of the presentinvention. It is often difficult to assess the extent of periodontaldisease or determine orthodontic information from a dental image, suchas intra-oral photos, X-rays, panoramic, or CBCT images. Sometimes theimages do not capture the full extent of dental anatomy necessary torender diagnostic or treatment decisions. Furthermore, sometimes patientsensitive information needs to be removed from an image and filled inwith missing synthetic information so that it is suitable for adownstream deep learning model. The system 1200 provides an inpaintingsystem that utilizes partial convolutions, adversarial loss, andperceptual loss. The inpainting system 1200 is particularly useful forrestoring missing portions of images to facilitate the identification ofcaries.

The system 1200 may be used to train a machine learning model to restoremissing data to images for use in pre-processing an image at step 108 ofthe method 100. In some embodiment, missing data may be restored to animage using the approach of FIG. 12 to obtain a corrected image and thecorrected image may then be reoriented using the approach of FIG. 3 toobtain a reoriented image (though the image output from the approach ofFIG. 3 may not always be rotated relative to the input image).Decontamination according to the approach of FIG. 5 may be performed andmay be performed on an image either before or after missing data isrestored to it according to the approach of FIG. 12.

In the system 1200, A training algorithm 1202 is trained using trainingdata entries including an image 1204 and a randomly generated mask 1206that defines portions of the image 1204 that are to be removed and whicha machine learning model 1210 is to attempt to restore. As for otherembodiments, the image 1204 of each training data entry may be accordingto any of the imaging modalities described herein. The trainingalgorithm 1202 may operate with respect to one or more loss functions1208 and modify the machine learning model 1210 in order to reduce theloss functions 1208 of the model 1210.

In the illustrated embodiment, the machine learning model 1210 is GANincluding a generator 1212 and a discriminator 1214. The generator 1212and discriminator may be implemented according to any of the approachesdescribed above with respect to the generators 512, 612, 618, 712, 812and discriminators 514, 614, 620, 714, 814 described above.

Training cycles of the machine learning model 1210 may include inputtingthe image 1204 and the random mask 1206 of a training data entry intothe generator 1212. The mask 1206 may be a binary mask, with one pixelfor each pixel in the image. The value of a pixel in the binary mask maybe zero where that pixel is to be omitted from the image 1204 and a onewhere the pixel of the image 1204 is to be retained. The image as inputto the generator 1212 may be a combination of the image 1204 and mask1206, e.g. the image 1204 with the pixels indicated by the mask 1206removed, i.e. replaced with random values or filled with a default colorvalue.

The generator 1212 may be trained to output a reconstructed syntheticimage 1216 that attempts to fill in the missing information in regionsindicated by the mask 1206 with synthetic imaging content. In someembodiments, the generator 1212 learns to predict the missing anatomicalinformation based on the displayed sparse anatomy in the input image1204. To accomplish this the generator 1212 may utilize partialconvolutions that only propagate information through the network that isnear the missing information indicated by the mask 1206. In someembodiments, the binary mask 1206 of the missing information may beexpanded at each convolutional layer of the network by one in alldirections along all spatial dimensions.

In some embodiments, the generator 1212 is a six multi-scale stage deepencoder-decoder generator and the discriminator 124 is a fivemulti-scale level deep discriminator. Each convolutional layer withinthe encoder and decoder stage of the generator 1212 may uses 4×4 partialconvolutions paired with batchnormalization and rectified linear unit(ReLU) activations. Convolutional downsampling may be used to downsampleeach multi-scale stage and transpose convolutions may be used toincrementally restore the original resolution of the input signal. Theresulting high-resolution output channels may be passed through a 1×1convolutional layer and hyperbolic tangent activation function toproduce the synthetic reconstructed image 1216.

At each iteration, the synthetic image 1216 and a real image 1218 from arepository may be passed through the discriminator 1214, which outputs arealism matrix 1220 in which each value of the realism matrix 1220 is avalue indicating which of the images 1216, 1218 is real.

The loss functions 1208 may be implementing using weighted L1 lossbetween the synthetic image 1216 and input image 1204 without masking.In some embodiments, the loss functions 1208 may further evaluateperceptual loss from the last three stages of the discriminator 1214,style loss based on the Gram matrix of the extracted features from thelast three stages of the discriminator, and total variation loss. Thediscriminator 1214 may be pretrained in some embodiments such that it isnot updated during training and only the generator 1212 is trained. Inother embodiments, the generator 1212 and discriminator 1214 may betrained simultaneously until the discriminator 1214 can no longerdifferentiate between synthetic and real images or a Nash equilibriumhas been reached.

During utilization, the discriminator 1214 may be discarded or ignored.An image to be reconstructed may be processed using the generator 1212.In some embodiments, a mask of the image may also be input as for thetraining phase. This mask may be generated by a human or automaticallyand may identify those portions of the image that are to bereconstructed. The output of the generator 1214 after this processingwill be a synthetic image in which the missing portions have been filledin.

In some embodiments, multiple images from multiple image modalities ormultiple images from a single modality may combined in an ensemble ofnetworks to form a comprehensive synthetic reconstructed image. Forexample, each image may be processed using a generator 1214 (which maybe trained using images of the imaging modality of the each image in thecase of multiple imaging modalities) and the output of the generators1214 may then be combined. The outputs may be combined by boosting,bagging, or other conventional machine learning methods such as randomforests, gradient boosting, or state vector machines (SVMs).

In at least one possible embodiment, the system 1200 may operate onthree-dimensional images 1204, such as a CT scan. This may includereplacing the 4×4 convolutional kernels with 4×4×4 convolutional kernelsand replacing the 1×1 convolutional kernels with 1×1×1 convolutionalkernels.

The training algorithm 1202 and utilization of the trained machinelearning model 1210 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 1200 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 4×4 and 1×1) with three-dimensionalconvolution kernels (e.g., 4×4×4 or 1×1×1).

Referring generally to FIGS. 3 through 12, the machine learning modelsthat are illustrated and discussed above are represented as CNNs.Additionally, specific CNN configurations are shown and discussed. Itshall be understood that, although both a CNN generally and the specificconfiguration of a CNN shown and described may be useful and well suitedto the tasks ascribed to them, other configurations of a CNN and othertypes of machine learning models may also be trained to perform theautomation of tasks described above. In particular a neural network ordeep neural network (DNN) according to any approach known in the art mayalso be used to perform the automation of tasks described above.

Referring to FIGS. 13 through 18, deep learning-based computer vision isbeing rapidly adopted to solve many problems in healthcare. However, anadversarial attack may probe a model and find a minimum perturbation tothe input image that causes maximum degradation of the deep learningmodel, while simultaneously maintaining the perceived image integrity ofthe input image.

In dentistry, adversarial attacks can be used to create maliciousexamples that compromise the diagnostic integrity of automated dentalimage classification, landmark detection, distortion correction, imagetransformation, text extraction, object detection, image denoising, orsegmentation models. Additionally, images might be manually tamperedwith in photoshop or other image manipulation software to fool aclinician into incorrectly diagnosing disease

Adversarial attacks have highlighted cyber security threats to currentdeep learning models. Similarly, adversarial attacks on medicalautomation systems could have disastrous consequences to patient care.Because many industries are increasingly reliant on deep learningautomation solutions, adversarial defense and detection systems havebecome a critical domain in the machine learning community.

There are two main types of adversarial defense approaches. One approachuses a screening algorithm to detect if an image is authentic and theother approach builds models that are robust against adversarial images.The quality of the defense system is dependent on the ability to createhigh quality adversarial examples.

To produce adversarial examples, attackers need to gain access to thesystem. Black box attacks assume no knowledge of model parameters orarchitecture. Grey box attacks have architectural information but haveno knowledge of model parameters. White box attacks have a prioriknowledge of model parameters and architecture. White box adversarialexamples may be used to evaluate the defense of each model, since whitebox attacks are the most powerful.

For white box attacks, an adversarial attacking system may beimplemented by building attacks directly on each victim model. In someembodiments, the attack system uses a novel variation of the projectedgradient decent (PGD) method (Madry Kurakin), which is an iterativeextension of the canonical fast gradient sign method (Goodfellow). PGDfinds the optimal perturbation by performing a projected stochasticgradient descent on the negative loss function.

For grey box attacks, an adversarial attacking system may be implementedby building attacks on the output of each victim model. Since grey boxattacks do not have access to the gradients of the model, the output ofeach victim model may be used to update the gradients of the attackingmodel. The attacking model therefore becomes progressively better atfooling the victim model through stochastic gradient decent.

For black box attacks, an adversarial attacking system may beimplemented by building attacks on the output of many victim models.Since black box attacks do not have access to the gradients of anymodel, the output of many victim models are used to update the gradientsof the attacking model. The attacking model therefore becomesprogressively better at fooling the victim model through stochasticgradient decent.

The systems disclosed herein may use adaptation of a coevolving attackand defense mechanism. After each epoch in the training routine, newadversarial examples may be generated and inserted into the trainingset. The defense mechanism is therefore trained to be progressivelybetter at accurate inference in the presence of adversarialperturbations and the attack system adapts to the improved defense ofthe updated model.

Referring specifically to FIG. 13, the illustrated system 1300 may beused to train a machine learning model to identify authentic andcorrupted images. In the system 1300, A training algorithm 1302 takes asinputs training data entries that each include an image 1304 and astatus 1306 of the image 1304, the status indicating whether the image1306 is contaminated or non-contaminated. The training algorithm 1302also evaluates a loss function 1308 with respect to a machine learningmodel 1310. In particular, the training algorithm 1302 adjusts themachine learning model 1310 according to whether the machine learningmodel correctly determines the status 1306 of a given input image 1304.

In the illustrated embodiment, the machine learning model 1310 is anadversarial detection CNN. The CNN may include attention-gated skipconnections and deep-supervision. In the illustrated embodiment, the CNNincludes nine multi-scale stages 1312 followed by a fully connectedlayer 1314 that outputs an authenticity score 1320. Each multi-scalestage 1312 may contain three 3×3 convolutional layers, paired withbatchnormalization and leaky rectified linear units (LeakyReLU). Thefirst and last convolutional layers of each stage 1312 may beconcatenated via dense connections which help reduce redundancy withinthe network by propagating shallow information to deeper parts of thenetwork. Each multi-scale stage 1312 may be downscaled by a factor oftwo at the end of each multi-scale stage 1312, such as by max pooling.The fifth and seventh multi-scale stages 1312 may be passed throughattention gates 1318 a, 1318 b before being concatenated with the last(ninth) stage 1312. The attention gate 1318 a may be applied to thefifth stage 1312 according to a gating signal derived from the seventhstage 1312. The attention gate 1318 b may be applied to the seventhstage 1312 according to a gating signal derived from the ninth stage1312. Not all regions of the image are relevant for estimatingperiodontal diagnosis, so attention gates may be used to selectivelypropagate semantically meaningful information to deeper parts of thenetwork. Adam optimization may be used during training whichautomatically estimates the lower order moments and helps estimate thestep size which desensitizes the training routine to the initiallearning rate.

In some embodiments, the images 1304 input to the network may beembodied as a raw 512×512 image 1304 and the output of the network maybe a likelihood score 1320 indicating a likelihood that the input image1304 is an adversarial example. The loss function 1308 may thereforedecrease with accuracy of the score. For example, where a high scoreindicates an adversarial input image, the loss function 1308 decreaseswith increase in the likelihood score 1320 when the input image 1304 isan adversarial image. The loss function 1308 would then increase withincrease in the likelihood score 1320 when the input image 1304 is notan adversarial image. The loss function 1308 may be implemented withcategorical cross entropy and Adam optimization may be used duringtraining which automatically estimates the lower order moments and helpsestimate the step size which desensitizes the training routine to theinitial learning rate.

The adversarial images 1304 in the training data set may be generatedwith any of projected gradient decent image contamination, syntheticallygenerated images, and manually manipulated images by licensed dentists.Because the adversarial detection machine learning model 1310 may besensitive to training parameters and architecture, a validation set maybe used for hyperparameter testing and a final hold out test set may beused to assess final model performance prior to deployment.

The training algorithm 1302 and utilization of the trained machinelearning model 1310 may be implemented using PYTORCH and AWS GPUinstances in the same manner as described above with respect to FIG. 3.

In at least one possible embodiment, the system 1300 operates onthree-dimensional images, such as a CT, by replacing two-dimensionalconvolutional kernels (e.g., 4×4 and 1×1) with three-dimensionalconvolution kernels (e.g., 4×4×4 or 1×1×1).

FIG. 14A is a schematic block diagram of a system 1400 a for protectinga machine learning model from adversarial input images 1402 inaccordance with an embodiment of the present invention. In particular,the system 1400 a includes a detector 1404 that evaluates theauthenticity of the input image 1402 and estimates whether the inputimage 1402 is adversarial. The detector 1404 may be implemented as themachine learning model 1310. If the image 1402 is found to beadversarial, the image is discarded as a contaminated image 1402

An adversarial network 1408 may receive an uncontaminated image 1410 andprocess the image 1410 to generate additive noise 1412 to contaminatethe input image in order to deceive a victim machine learning model1414. The victim model 1414 may be any machine learning model describedherein or any machine learning model trained to transform images orgenerate inferences based on images. Each image 1410 may have anaccurate prediction associated with an input image 1410 may be aprediction obtained by processing the input image 1410 using the victimmodel 1414 without added noise 1412 or according to labeling by someother means, such as by a human with expertise.

The noise 1412 is combined with the image 1410 to obtain thecontaminated input image 1402 that is input to the detector 1404. Thedetector 1404 attempts to detect these adversarial images 1402 anddiscard them. Input images 1402 that are not found to be adversarial arethen input to the machine learning model 1414 that outputs a prediction1416. The prediction 1416 is more robust due to the presence of thedetector 1404 inasmuch as there is more assurance that the image 1402 isnot adversarial.

Referring to FIG. 14B, in some embodiments the illustrated system 1400 bmay be used to train an adversarial network 1408 to generate noise 1412for contaminating input images 1410. This may be with the intent ofgenerating adversarial images for training purposes, such as fortraining the machine learning model 1310. In other applications,adversarial images may be generated from patient images in order toprotect patient privacy, e.g., prevent automated analysis of thepatient's images. Accordingly, the detector 1404 may be omitted in theembodiment of FIG. 14b in order to expose the victim model 1414 to theadversarial images and assess its response.

The loss function of the adversarial network 1408 may be based on theprediction 1414, i.e. if the loss function decreases with increasinginaccuracy of the prediction. For example, the input image 1408 may bepart of a training data entry including an accurate prediction. Thedifference between the prediction 1414 and the accurate prediction maytherefore be evaluated to determine the output of the loss function thatis used to update the adversarial network.

In some embodiments, the loss function is a loss function 1418 that hastwo goal criteria minimizing 1420 noise and minimizing 1422 modelperformance, i.e. maximizing inaccuracy of the prediction 1416.Accordingly, the loss function 1418 may be a function of inaccuracy ofthe prediction 1416 relative to an accurate prediction associated withthe input image 1408 and is also be a function of the magnitude of theadversarial noise 1412. The loss function 1418 therefore penalizes theadversarial network 1408 according to the magnitude of the noise andrewards the adversarial network 1408 according to degradation ofaccuracy of the victim model 1414.

The adversarial network 1408 and its training algorithm may beimplemented according to any of the machine learning models describedherein. In particular, the adversarial network 1408 may be implementedas a generator according to any of the embodiments described herein. Insome embodiments, the adversarial network 1408 utilizes a sixmulti-scale level deep encoder-decoder architecture. Each convolutionallayer within the encoder and decoder stage of the networks may use three3×3 convolutions paired with batchnormalization and rectified linearunit (ReLU) activations. Convolutional downsampling may be used todownsample each multi-scale level and transpose convolutions may be usedto incrementally restore the original resolution of the input signal.The resulting high-resolution output channels may be passed through a1×1 convolutional layer and hyperbolic tangent activation function toproduce adversarial noise 1412, which may be in the form of an image,where each pixel is the noise to be added to the pixel at that positionin the input image 1410. At each iteration, the adversarial noise 1412may be added to an image 1410 from a repository of training data entriesto obtain the contaminated input image 1402. The contaminated inputimage 1402 may then be processed using the victim model 1414. Thetraining algorithm may update model parameters of the adversarialnetwork 1408 according to the loss function 1418. In some embodiments,the loss function 1418 is a function of mean squared error (MSE) of theadversarial noise 1412 and inverse cross entropy loss of the victimprediction 1416 relative to an accurate prediction associated with theinput image 1408. In some embodiments, the victim model 1414 (e.g.,machine learning model 1310) and the adversarial network 1408 may betrained concurrently.

FIG. 14C is a schematic block diagram of a system 1400 c for training amachine learning model to be robust against attacks using adversarialimages in accordance with an embodiment of the present invention. In theillustrated embodiment, a contaminated image 1402, such as may begenerated using an adversarial network, is processed using the victimmodel 1414, which outputs a prediction 1416. A training algorithmevaluates a loss function 1424 that decreases with accuracy of theprediction, e.g., similarity to a prediction assigned to the input image1410 on which the contaminated image 1402 is based. The trainingalgorithm then adjusts parameters of the model 1414 according to theloss function 1424. In the illustrated embodiment, the model 1414 mayfirst be trained on uncontaminated images 1410 until a predefinedaccuracy threshold is met. The model 1414 may then be further trainedusing the approach of FIG. 14C in order to make the model 1414 robustagainst adversarial attacks.

FIG. 14D is a schematic block diagram of a system 1400 d for modifyingadversarial images to protect a machine learning model from corruptedimages in accordance with an embodiment of the present invention. In theillustrated embodiment, input images 1402, which may be contaminatedimages are processed using a modulator 1426. The modulator adds smallamounts of noise to the input image to obtain a modulated image. Themodulated image is then processed using the machine learning model 1414to obtain a prediction 1416. The prediction is made more robust inasmuchas subtle adversarial noise 1412 that is deliberately chosen to deceivethe model 1414 is combined with randomized noise that is not selected inthis manner. The parameters defining the randomized noise such asmaximum magnitude, probability distribution, and spatial wavelength(e.g., permitted rate of change between adjacent pixels) of the randomnoise may be selected according to a tuning algorithm. For example,images 1402 based on images 1410 with corresponding accurate predictionsmay be obtained using an adversarial network 1408, such as using theapproach described above with respect to FIG. 14B. The images 1410 maybe modulated by modulator 1426 and processed using the model 1414 toobtain predictions. The accuracy of this prediction 1416 may beevaluated, noise parameters modified, and the images 410 processed againiteratively until noise parameters providing desired accuracy of theprediction 1416 is achieved.

For example, a low amount of randomized noise may not be sufficient tointerfere with the adversarial noise 1412, resulting in greater errorsrelative to an intermediate amount of noise that is greater than the lowamount. Likewise, where a larger amount of noise greater than theintermediate amount is used, accuracy of the machine learning model 1414may be degraded due to low image quality. Accordingly, the tuningalgorithm may identify intermediate values for the noise parameters thatbalance adversarial noise disruption with image quality degradation.

In some embodiments, the modulator 1426 is a machine learning model. Themachine learning model may be a generator, such as according to any ofthe embodiments for a generator described herein. The modulator 1426 maytherefore be trained using a machine learning algorithm to generatenoise suitable to disrupt the adversarial noise 1412. For example,training cycles may include generating a contaminated input image 1402as described above, processing the contaminated input image 1402 usingthe modulator 1426 to obtain a modulated input. The modulated input isthen processed using the model 1414 to obtain a prediction 1416. A lossfunction that decreases with increase in the accuracy of the prediction1416 relative to the accurate prediction for the image 1410 used togenerate the contaminated input image 1402 may then be used to tune theparameters of the modulator 1426.

FIG. 14E is a schematic block diagram of a system 1400 e for dynamicallymodifying a machine learning model to protect it from adversarial imagesin accordance with an embodiment of the present invention.

In the illustrated embodiment, input images 1402, which may becontaminated with adversarial noise 1412 are processed using a dynamicmachine learning model 1428. In this manner, the ability to train theadversarial network 1408 to deceive the model 1428 is reduced relativeto a static machine learning model 1414.

The dynamic machine learning model 1428 may be implemented using variousapproaches such as:

-   -   The parameters of a machine learning model 1414 as described        above are dynamically modified by different random noise each        time the model 1414 outputs a prediction 1416, with the noise        parameters of the random noise (maximum magnitude, probability        distribution, etc.) being selected such that accuracy of the        model 1414 is maintained within acceptable levels. The random        variations of the parameters impairs the ability of the        adversarial network 1408 to generate adversarial noise 1412 that        is both undetectable and effective in deceiving the model 1414.    -   A plurality of machine learning models 1414 are independently        trained to generate predictions 1416. Due to the stochastic        nature of the training of machine learning models, the        parameters of each machine learning model 1414 will be        different, even if trained on the same sets of training data.        Alternatively, different training data sets may be used for each        machine learning model 1414 such that each is slightly different        from one another. In yet another alternative, hyperparameters or        other parameters that govern training of each model may be        deliberately set to be different from one another. In yet        another alternative, different types of machine learning models        1414 (DNNs and CNNs) or differently structured machine learning        models (different numbers of stages, differently configured        stages, different attention gate configurations, etc.) may be        used in order to ensure variation among the machine learning        models 1414. The dynamic model 1428 may then (a) randomly select        among a plurality of models 1414 to make each prediction        1416, (b) combine predictions 1416 from all or a subset of the        models 1414 and combine the predictions 1416, (c) apply random        weights to the predictions 1416 from all or a subset of the        models 1414 and combine the weighted predictions to obtain a        final prediction that is output from the dynamic model 1428.

Referring to FIGS. 15 through 19, cross-institutional generalizabilityof AI models is hampered in dentistry because of privacy concerns. Inaddition, patient datasets from a clinic in Georgia might differsubstantially from clinics in New York or San Francisco. A model trainedon a dataset in one region might not perform well on patient populationsoriginating from a different region of the world because clinicalstandards, patient demographics, imaging hardware, image acquisitionprotocols, software capabilities, and financial resources can varydomestically and internationally. Dentistry is particularly prone tocross-institutional variability because of the lack of clinicalstandardization and high degree of differentiation in oral hygienepractices among different patient populations.

Training dental AI models to reach cross-institutional generalizabilityis challenging from a data management and artificial intelligence (AI)model management perspective because in order to establish the correcttreatment protocol or diagnosis many different data sources are oftencombined. To obtain the correct codes on dental procedures, dental imageanalytics may be combined with patient metadata, such as clinicalfindings, Decayed-Missing-Filled-Treated (DMFT) information, age, andhistorical records. However, in many cases the past medical history isnot known or is not stored in a single place. Protected, disparate,restricted, fragmented, or sensitive patient information hindersaggregation of patient medical history.

To overcome this challenge, the approach described below with respect toFIGS. 15 through 19 may be used to allows models to learn from disparatedata sources and achieve high cross-institutional generalizability whilepreserving the privacy of sensitive patient information.

Referring specifically to FIG. 15, in a typical implementation, theremay be a central server 1500 that trains a machine learning model withrespect to data from various institutions 1502. The institutions 1502may be an individual dental clinic, a dental school, a dental-insuranceorganization, an organization providing storage and management of dentaldata, or any other organization that may generate or store dental data.The dental data may include dental images, such as dental imagesaccording to any of the two-dimensional or three-dimensional imagingmodalities described hereinabove. The dental data may includedemographic data (age, gender) of a patient, comorbidities, clinicalfindings, past treatments, Decayed-Missing-Filled-Treated (DMFT)information, and historical records.

As discussed below, a machine learning model may be trained on site ateach institution with coordination by the central server 1500 such thatpatient data is not transmitted to the central server 1500 and thecentral server 1500 is never given access to the patient data of eachcentral server 1500.

Referring to FIGS. 16 and 17, a method 1600 may include training 1602individual machine learning models 1702 at each institution 1502 using adata store 1704 of that institution, the data store storing any of thedental data described above with respect to FIG. 15. Note thatprocessing “at each institution 1502” may refer to computation using acloud-based computing platform using an account of the institution suchthat the data store 1704 is accessible only by the institution and thoseallowed access by the institution. This may be any machine learningmodel trained using any algorithm known in the art, such as a neuralnetwork, deep neural network, convolution neural network, or the like.The machine learning model may be a machine learning model according toany of the approaches described above for evaluating a dental feature(tooth, JE, GM, CEJ, bony points), dental condition (PD, CAL), ordiagnose a dental disease (e.g., any of the periodontal diseasesdescribed above). The machine learning model may also be trained toidentify bone level, enamel, dentin, pulp, furcation, periapical lines,orthodontic spacing, temporal mandibular joint (TMJ) alignment, plaque,previous restorations, crowns, root canal therapy, bridges, extractions,endodontic lesions, root length, crown length, or other dental featuresor pathologies.

The machine learning models 1702 trained by each institution 1502 may betransmitted 1604 to the central server 1500, which combines 1606 themachine learning models 1702 to obtain a combined static model 1706.Combination at step 1606 may include bagging (bootstrap aggregating) themachine learning models 1702. For example, the combined static model1706 may be utilized by processing an input using each machine learningmodel 1702 to obtain a prediction from each machine learning model 1702.These predictions may then be combined (e.g., averaged, the mostfrequent prediction selected, etc.) to obtain a combined prediction.Alternatively, the machine learning models 1702 themselves may beconcatenated to obtain a single combined static machine learning model1706 that receives an input and outputs a single prediction for thatinput.

The combined static model 1706 may then be transmitted 1608 by theserver system 1500 to each of the institutions 1502.

Referring to FIG. 18, while still referring to FIG. 17, a method 1800may be used to train a combined moving model 1708. The combined movingmodel 1708 is combined by the server system 1500 with the combinedstatic model 1706 to obtain a combined prediction 1710 for a given inputduring utilization. The combined moving model 1708 may be trained bycirculating the combined moving model 1708 among the plurality ofinstitutions 1502 and training the combined moving model 1708 incombination with the combined static model 1706 at each of theinstitutions 1502. This may be performed in the manner described belowwith respect to step 1806.

For example, the method 1800 may include the central server 1500generating 1801 an initial moving base model that is used as thecombined moving model 1708 in the first iteration of the method 1800.The initial moving base model may be populated with random parameters toprovide a starting point for subsequent training. Alternatively, theinitial moving base model may be trained using a sample set of trainingdata. This initial training may include training the initial moving basemodel in combination with the combined static model 1706

One or more institutions 1502 are then selected 1802 by the centralserver 1500, for example, from 1 to 10 institutions. Where a singleinstitution 1500 is processed at each iteration of the method 1800, themethod 1800 may proceed differently as pointed at various points in thedescription below. The groups of institutions 1500 selected may bestatic, i.e. the same institutions will be selected as a group wheneverthat group is selected, or dynamic, i.e. each selection at step 1802until a predefined number of institutions have been selected.

The selection at step 1802 may be performed based on various criteria.As will be discussed below, the moving base model as trained at eachinstitution may be transmitted among multiple institutions. Accordingly,the latency required to transmit data among the institutions 1502 may beconsidered in making the selection at step 1802, e.g., a solution to thetraveling salesman problem may be obtained to reduce the overall latencyof transmitting the moving base model among the institutions 1502. Insome embodiments, step 1802 may include selecting one or moreinstitutions based on random selection with the probability of selectionof each institution 1502 being a function of quality of data (increasingprobability of selection with increasing quality) and time since theeach institution 1502 was last selected according to the method 1800(increasing probability of selection with increasing time since lastselection). Quality of data may be a metric of the institution 1502indicating such factors as authoritativeness in field (e.g., esteemedinstitution in field of dentistry), known accuracy, known compliancewith record-keeping standards, known clean data (free of defects),quantity of data available, or other metric of quality.

The method 1800 may then include the central server 1500 transmitting1804 the moving base model to the selected institutions 1502. For thefirst iteration of the method 1800, this may include transmitting theinitial moving base model to the selected institutions 1502. Otherwise,it is the combined moving model 1708 resulting from a previous iterationof the method 1800.

Each institution 1402 then trains a moving base model 1712 that isinitially a copy of the base model received at step 1804, which is thencombined with the combined static model 1706 transmitted to theinstitutions 1502 at step 1608. For example, each of the moving basemodel 1712 and the combined static model 1706 may include multiplelayers, including multiple hidden layers positioned between a firstlayer and a last layer, such as a deep neural network, convolutionneural network, or other type of neural network. One or more layersincluding the last layer and possibly one or more layers immediatelypreceding the last layer are removed from the combined static model1706. For example, where the combined static model 1706 is a CNN, thefully connected layer and possibly one or more of the multi-scale stagesimmediately preceding it may be removed.

The outputs of the last layer remaining of the combined static model1706 is then concatenated with outputs of a layer of the moving basemodel 1712 positioned in front of a final layer (e.g., a fully connectedlayer), e.g. at least two layers in front of the final layer(hereinafter “the merged layer”). For example, the combined static model1706 (prior to layer removal) and the moving base model 1712 may beidentically configured, e.g. same number of stages of the same size. Forexample, each may be a CNN having the same number of stages with thestarting stages being of the same size, the same downsampling betweenstages, and each ending with a fully connected layer. However, in otherembodiments, the models 1706, 1712 may have different configurations.

Concatenating outputs of the final layer of the truncated combinedstatic model 1706 with the outputs of the merged layer may include acombined output that has double the depth of the outputs of the finallayer and merged layer individually. For example, where the final layerhas a 10×10 output with a depth of 100 (10×10×100) would become a10×10×200 stage following concatenation. In other embodiments, theoutputs of the final layer and merged layer may be concatenated andinput to a consolidation layer such that the depth output from theconsolidation layer is the same as the output of the merged layer (e.g.10×10×100 instead of 10×10×200). The consolidation layer may be amachine learning stage, e.g. a multi-scale network stage followed bydownsampling by a factor of 2, such that training of the combined staticmodel 1706 and moving base model 1712 includes training theconsolidation layer to select values from the final layers of thetruncated models to output from the consolidation layer.

The moving base model 1712 as combined with the combined static model1706 may then be trained 1806 at the selected institution 1502. This mayinclude, for each training data entry of a plurality of training dataentries, an input to the first stage of the combined static model 1706and the moving base model 1712 to obtain a prediction 1714. The trainingdata may be the same as or different from the training data used totrain the static models at step 1602. The parameters of the moving basemodel 1712 may then be modified according to the accuracy of thepredictions 1714 for the training data entries, e.g. as compared to thedesired outputs indicated in the training data entries. The parametersof the combined static model 1706 may be maintained constant. The mannerin which the moving base model 1712 and combined static model 1706 arecombined may be as described in the following paper, which is herebyincorporated herein by reference in its entirety:

-   Kearney, V., Chan, J. W., Wang, T., Perry, A., Yom, S. S., &    Solberg, T. D. (2019). Attention-enabled 3D boosted convolutional    neural networks for semantic CT segmentation using deep supervision.    Physics in Medicine & Biology, 64 (13), 135001.

The method 1700 may include returning 1808 gradients obtained during thetraining at step 1806 to the server system 1500. As known in the art,the weights and other parameters of a machine learning model may beselected according to gradients. These gradients change over time inresponse to evaluation of a loss function with respect to a predictionfrom the machine learning model in response to an input of a trainingdata entry and a desired prediction indicating in the training dataentry. Accordingly, the gradients of the moving base model 1712 asconstituted after the training step 1806 may be returned 1808 to thecentral server. Note that since gradients are of interest and are whatis provided to the central server 1500 in some embodiments, the trainingstep 1806 may be performed up to the point that gradients are obtainedbut the moving base model 1712 is not actually updated according to thegradients.

The gradients from the multiple institutions selected at step 1802 maythen be combined by the server system 1500 to obtain combined gradients,e.g. by averaging the gradients to obtain averaged gradients. Thecombined gradients may then be used to select new parameters for thecombined moving model 1708 and the combined moving model 1708 is thenupdated according to the new parameters.

FIG. 19 illustrates an approach 1900 for combining gradients from eachmoving base model 1712 at each institution 1502. Each institution 1502trains the moving base model 1712 using its data store 1704 to obtainbase gradients 1902 that define how to modify the parameters of themoving base model 1712 in subsequent iterations. The base gradients 1902are returned to the central server 1500 that combines the base gradients1902 to obtain combined gradients 1904. These combined gradients 1904are then used to update the combined moving model 1708 on the server.The combined moving model 1708 as updated is then transmitted to theinstitutions 1502 and used and the moving base model 1712 in the nextiteration of the method 1800. Note that the institutions 1502 thatreceive the updated combined moving model 1708 may be different fromthose that provided the base gradients 1902 since different institutions1502 may be selected at each iteration of the method 1800.

Returning again to FIG. 18, the method 1800 may include the centralserver 1500 evaluating 1812 model convergence. For example, eachinstitution selected at step 1802 may return values of the loss functionof the training algorithm for inputs processed using the moving basemodel 1712 during the training step 1806. The central server 1500 maycompare the values of the loss function (e.g., an average or minimum ofthe multiple values reported) to the values returned in a previousiteration to determine an amount of change in the loss function (e.g.compare the minimum loss function values of the current and previousiteration).

The method 1800 may include selecting a learning period 1814 accordingto the rate of convergence determined at step 1812. The learning periodmay be a parameter defining how long a particular institution 1502 isallowed to train 1806 its moving base model 1712 before its turn endsand the selection process 1802 is repeated. As the rate of convergencebecomes smaller, the learning period becomes longer. Initially, the rateof convergence may be high such that new institutions 1502 are selected1802 at first intervals. As the rate of convergence falls, institutions1502 are selected 1802 at second intervals, longer than the firstintervals. This allows for a highly diverse training sets at initialstages of training, resulting in more rapid training of the combinedmoving model 1708. Enforcement of the learning period may be implementedby the central server 1500 by either (a) instructing each institution1502 to perform the training step 1806 for the learning period or (b)instructing the institution 1502 to end the training step 1806 uponexpiry of the learning period following selection 1802 or some timepoint after selection of the institution 1502.

The method 1800 may then repeat from step 1802 with selection 1802 ofanother set of institutions 1502. Since the selection 1802 is random, itis possible that one or more of the same institutions 1502 may beincluded in those select in the next iteration of the method 1800.

In embodiments where a single institution 1502 is selected at step 1802,step 1810 may be modified. For example, the institution may send thegradients of the moving base model 1712 to the central server, whichthen updates the parameters of the combined moving model 1708 accordingto the gradients without the need to combine the gradients with those ofanother institution. Alternatively, parameters of the moving base model1712 may be updated by the institution according to the training step1806 and the moving base model 1712 may be transmitted to the centralserver 1500, which then uses the moving base model 1712 as the combinedmoving model 1708 for a subsequent iteration of the method 1800. Sincethe institution 1502 may update the combined moving model 1708, theinstitution 1502 may transmit the combined moving model 1708 to anotherinstitution 1502 selected by the server system 1500 rather than sendingthe updated combined moving model 1708 to the server system 1500.

When the combination of the combined static model 1706 and the combinedmoving model 1708 have reached a desired level of accuracy and/or haveconverged (i.e., change between iterations of the method 1800 is below apredefined convergence threshold or threshold condition), thecombination may then be used to generate combined predictions 1710either on the server system 1500 or by transmitting the latest versionof the combined moving model 1708 to the institutions such that they maygenerate predictions along with their copy of the combined static model.The combined moving model 1708 may be combined with the combined staticmodel 1706 in the same manner as described above with respect to step1806 for combining the moving base model 1712 with the combined staticmodel 1706, i.e. truncating the combined static model 1706 to obtain atruncated model and concatenating the outputs of the truncated modelwith outputs of an intermediate layer of the combined moving model 1708.

The approach of FIG. 18 may have the advantage that, when the combinedstatic model 1706 is maintained constant, catastrophic forgetting thatmight result from only sequential training is reduced. Likewise, whereonly the parameters of the combined moving model 1708 are updated, theprocessing of batches of training data at each iteration at aninstitution 1500 is speeded up and batch size may be increased. The onlyprocessing using the combined static model 1706 is a forward pass ofinput data and computation of gradients or new parameters can be omittedfor the combined static model 1706.

FIG. 20 includes a schematic representation of dental anatomy that maybe represented in a dental image according to any of the imagingmodalities described herein. For example, one or more teeth 2000 may berepresented. Each tooth 2000 may have a CEJ 2002 that can be measured atvarious points around the tooth 2000. A GM, e.g., gum line, 2004 mayalso be represented along with the bone level 2006. Parts of a the teeth2000 such as pulp 2008 and dentin 2010 may also be identified. Cariouslesions (e.g., caries or cavities) 2012 may also be represented.

A machine learning model, such as any of the architectures describedherein for labeling teeth (see, e.g., the approach of FIG. 8) may beused to label dental anatomy. Likewise, the approaches described abovefor measuring features of dental anatomy (see, e.g., the approach ofFIGS. 9 and 10) may be used to measure dental anatomy. In particular,training data entries including images (inputs) and labels of the dentalanatomy (desired output) may be used to train a machine learning modelto output dental anatomy labels for a given input image, such asaccording to the approaches described hereinabove. Likewise, trainingdata entries including images and labels of dental anatomy (input) andlabels of measurements of dental anatomy (desired output) may be used totrain a machine learning model to output measurements for a given inputimage with its corresponding labels of dental anatomy, such as accordingto the approaches described herein above. In particular, the machinelearning model may be a CNN. However, other machine learning approaches,such as random forest, gradient boosting, support vector machine, or thelike may also be used.

For a given item of dental anatomy, such as any of those referencedherein, particularly those referenced with respect to FIG. 20, one ormore machine learning models may be trained to measure that item ofdental anatomy. Measurements of an item of dental anatomy may includeits center of mass, relative distance to other anatomy, size distortion,and density.

For a carious lesion 2012 in a tooth 2000, machine learning models maybe trained to obtain the following measurements of the carious lesion2012: volume, area, distance to pulp, percent of tooth covered by it,distance into dentin, involved surfaces of the tooth, and identifier ofthe affected tooth. Machine learning models may also be trained toidentify fillings or other restorations on teeth and their measurementssuch as volume, area, percent of tooth covered by it, involved surfacesof the tooth, material, type, and identifier of the affected tooth.

Machine learning models may be trained to identify and measureperiodontal anatomy such as distal gingival margin, mesial gingivalmargin, distal CAL, mesial CAL, distal PD, mesial PD, distal bone level,mesial bone level, and the identifier of the tooth for which theperiodontal anatomy is identified and measured.

Machine learning models may be trained to identify and measure dentalanatomy that may be used to determine the appropriateness of root canaltherapy at a given tooth position such as crown-to-root-ratio, calculus,root length, relative distance to adjacent teeth, furcation, fracture,and whether the tooth at that tooth position is missing.

The manner in which a machine learning model is trained to perform anyof these measurements may be as described above with respect to FIG. 10except that any of the above-described measurements may be used in theplace of pocket depth. Likewise, additional or alternative labels (e.g.,pixel masks) of features in an image may be used, such as labels forcaries, restorations on caries, or defects in restorations as describedbelow.

FIG. 21 is a schematic block diagram of a system 2100 for identifyingperturbations to anatomy labels in accordance with an embodiment of thepresent invention. The system 2100 may include an encoder network 2102.The encoder network 2102 may include a number of multi-scale stages withdownsampling between them with the last stage coupled to a fullyconnected layer. The encoder network 2102 may be implemented accordingto any of the approaches described above for implementing a CNN. Othermachine learning approaches may also be used, such as random forest,gradient boosting, or support vector machine.

Training data entries may each include an image 2104, such as an imageof dental anatomy according to any of the imaging modalities describedherein. Each training data entry may further include an anatomy label2106, which may be a label of any dental anatomy (including caries orother dental pathologies) as described herein. Each training data entrymay further include a perturbation style 2108. The perturbation style2108 includes an adjustment to boundaries of the anatomy label (e.g.,pixel mask) 2106. In particular, the perturbation style 2108 may includeerosion, e.g., shrinking of the image area occupied by the label 2106,dilation, e.g. expanding the image area occupied by the label 2106,increasing roughness of the boundary of the label 2106, or increasingsmoothness of the boundary of the label 2106, or changing anotherproperty of the label 2016. The perturbation style 2108 may berepresented in a predefined format, e.g. a numerical value indicatingthe type of the perturbation (erode, dilate, roughen boundary, smoothboundary) and a degree of the perturbation (amount of erosion, amount ofdilation, amount of roughening, amount of smoothing). The values may beinterpreted according to a perturbation algorithm that implements thetype and the degree of perturbation on a given input label.

The label 2106 may be adjusted according to the perturbation style 2108(eroded, dilated, roughened, or smoothed), such as using theperturbation algorithm, to obtain a perturbed anatomy label 2110. Theperturbed anatomy label 2110 and image 2104 are concatenated and inputto the encoder 2102 that outputs an estimated perturbation style. Theloss function may therefore increase with an increase in the differencebetween the estimated perturbation style 2112 and the perturbation style2108 of the training data entry. Accordingly, the training algorithm mayprocess training data entries and adjust parameters of the encoder 2102according to the loss function to train the encoder 2102 to determinethe perturbation style 2108 for a given input image.

Following training, an image 2014 and anatomy label 2106 may beprocessed using the encoder 2102 to obtain an estimated perturbationstyle of the image. Perturbation styles for a set of images, each havingan anatomy label, may be obtained using the encoder 2102 and theperturbation styles may be aggregated, e.g. averaged, to characterizethe approach to labeling of a source of the set of images. For example,the images may be images labeled by an individual dental professional ordental professionals in a given geographic region (e.g., city, state, orcountry).

FIG. 22 is a schematic block diagram of another system 2200 foridentifying perturbations to anatomy labels in accordance with anembodiment of the present invention. The system 2200 may include anencoder network 2202. The encoder network 2202 may include a number ofmulti-scale stages with downsampling between them. The encoder network2202 may be implemented according to any of the approaches describedabove for implementing a CNN. However, in the illustrated embodiment,the fully connected layer is omitted and the output of the last stage isa matrix of values, such as 4×4 matrix. The encoder 2202 may be anencoder 2102 trained as described above with respect to FIG. 22 exceptthat, following training, the fully connected (FC) layer is removed.Accordingly, an input image 2204 and a label 2206 of anatomy (e.g.,pixel mask) are concatenated and processed using the encoder 2202 toobtain a style matrix 2208 that encodes attributes of the label that canbe used to characterize a labeling style of the individual that createdthe label 2206. The encoder 2202 may also be implemented using anothermachine learning approach, such as random forest, gradient boosting, orsupport vector machine.

Style matrices may be obtained for a set of images, each having ananatomy label, using the encoder 2202 and the style matrices may beaggregated, e.g. averaged, to characterize the approach to labeling of asource of the set of images. For example, the images may be imageslabeled by an individual dental professional or dental professionals ina given geographic region (e.g., city, state, or country).

FIG. 23 is a schematic block diagram of a system 2300 for identifyingcaries based on anatomy labeling style in accordance with an embodimentof the present invention. The system 2300 includes a generator 2302coupled to a discriminator 2304. The generator 2302 may be anencoder-decoder and the discriminator 2304 may be an encoder. Thegenerator 2302 and discriminator 2304 may be implemented and trainedusing any of the approaches described herein for implementing agenerator and discriminator of a GAN, such as using CNNs. Other machinelearning approaches may also be used, such as random forest, gradientboosting, or support vector machine.

The generator 2302 takes as inputs an image 2306, a tooth label 2308(e.g., pixel mask showing pixels representing a tooth), and arestoration label 2310 (e.g., pixel mask showing pixels representing arestoration on the tooth). These inputs are concatenated and processedusing the generator 2302 to obtain a synthetic caries label 2312, e.g. apixel mask showing one or more caries corresponding to the dental image,tooth of interest, and corresponding restoration represented by thelabel 2310, 2308, 2306. The synthetic caries label 2312 may be inputwith a real caries label 2314 to the discriminator 2404. The real carieslabel 2314 may be a pixel mask for one or more caries represented in anunpaired dental image (not the image 2306 or an image of the sameanatomy represented in the image 2306). The synthetic caries label 2312and real caries label 2314 are input to the discriminator 2304 thatoutputs a realism matrix 2316 such that each value of the realism matrixis an estimate as to which of the labels 2312, 2314 is real. As forother embodiments described herein, an aggregation (average, mostfrequent estimate) may be used by a loss function of the trainingalgorithm.

The synthetic caries label 2312 may also be compared to a target carieslabel 2318 that is a pixel mask labeling one or more caries representinga ground truth caries label. The result of this comparison is agenerator loss 2320 that increases with increase in differences betweenthe labels 2312, 2318. Accordingly, the generator 2302 may be trained bya training algorithm that adjusts the generator 2302 to reduce thegenerator loss 2320 and to increase the likelihood that the realismmatrix 2316 will indicate that the synthetic caries 2312 are real. Thetraining algorithm likewise trains the discriminator 2304 to correctlyidentify the synthetic caries labels 2312 as fake. Training may continueuntil the generator loss 2320 converges and the discriminator 2304cannot distinguish between the synthetic and real caries labels 2312,2314 or Nash equilibrium is reached.

As shown in FIG. 23, training may additionally be performed withreference to an individual style matrix 2322 (style matrix for anindividual labeler) and/or a geography style matrix 2324 (style matrixfor labelers within a geographic region) of a training data entry. Thematrices 2322, 2324 may be obtained using the system 2200 for thelabeler that generated the target caries labels 2318 for the images2306. The style matrices 2322, 2324 may be concatenated with one anotherand with an output of one of the stages of the generator 2302 and theresult of the concatenation may then be input to the next stage of thegenerator 2302. For example, the matrices 2322, 2324 may be concatenatedwith the output of the stage 2326 that is the last stage of the encoderand the first stage of the decoder of the generator 2302.

During training, each training data entry may therefore include asinputs image 2306, a tooth label 2308, restoration label 2310, and oneor both of a style matrix 2322 and geography style matrix 2324 for thelabeler that generated the labels 2308, 2310, 2318. Each training dataentry may also include a target caries label 2318 as a desired output ofthe training data entry. In this manner, the generator 2302 is trainedto identify caries while taking into account variations in labelingbehaviors of individuals and populations in a given geographic area.

FIG. 24 is a schematic block diagram of a system 2400 for detectingdefects in a restoration in accordance with an embodiment of the presentinvention. The system 2400 includes a generator 2402 coupled to adiscriminator 2404. The generator 2402 may be an encoder-decoder and thediscriminator 2404 may be an encoder. The generator 2402 anddiscriminator 2404 may be implemented and trained using any of theapproaches described herein for implementing a generator anddiscriminator of a GAN, such as CNNs. Other machine learning approachesmay also be used, such as random forest, gradient boosting, or supportvector machine.

The generator 2402 takes as inputs an image 2406, a tooth label 2408(e.g., pixel mask showing pixels representing a tooth), and arestoration label 2410 (e.g., pixel mask showing pixels representing arestoration on the tooth), and a caries label 2412 (e.g., pixel maskshowing pixels representing one or more caries repaired by therestoration shown by the label 2410). These inputs are concatenated andprocessed using the generator 2402 to obtain a synthetic defect label2414, e.g. a pixel mask showing defects in the restoration shown bylabel 2410. Defects in a restoration, such as a filling, crown, rootcanal, veneer, or other restoration may include erosion around the edgesof a filling, decay around a crown, a root canal that is notsufficiently deep, endodontic disease around a root canal, void or opencontact around the filling or crown, fracture of the filling or crown,incorrect fitting of a crown or filling, compromised restorationmaterial such as the liner or base, or other decay around therestoration.

The synthetic defect label 2414 may be input with a real defect label2416 to the discriminator 2404. The real defect label 2416 may be apixel mask for one or more defects represented in an unpaired dentalimage (not the image 2406 or an image of the same anatomy represented inthe image 2406). The synthetic defect label 2414 and real caries label2416 are input to the discriminator 2404 that outputs a realism matrix2418 such that each value of the realism matrix is an estimate as towhich of the labels 2414, 2416 is real.

The synthetic defect label 2414 may also be compared to a target defectlabel 2420 that is a pixel mask labeling one or more defects of therestoration represented in the restoration label 2410. The result ofthis comparison is a generator loss 2422 that increases with increase indifferences between the labels 2414, 2420. Accordingly, the generator2402 may be trained by a training algorithm that adjusts the generator2402 to reduce the generator loss 2422 and to increase the likelihoodthat the realism matrix 2316 will indicate that the synthetic defectlabels 2414 are real. The training algorithm likewise trains thediscriminator 2404 to correctly identify the synthetic defect labels2414 as fake. Training may continue until the generator loss 2422converges and the discriminator 2404 cannot distinguish between thesynthetic and real defect labels 2414, 2416 or Nash equilibrium isreached.

As shown in FIG. 24, training may additionally be performed withreference to an individual style matrix 2424 (style matrix for anindividual labeler) and/or a geography style matrix 2426 (style matrixfor labelers within a geographic region) of a training data entry. Thematrices 2424, 2426 may be obtained using the system 2200 for thelabeler that generated the target defect labels 2420 for the images2406. The style matrices 2424, 2426 may be concatenated with one anotherand with an output of one of the stages of the generator 2402 and theresult of the concatenation may then be input to the next stage of thegenerator 2402. For example, the matrices 2424, 2426 may be concatenatedwith the output of the stage 2428 that is the last stage of the encoderand the first stage of the decoder of the generator 2402.

During training, each training data entry may therefore include asinputs an image 2406, a tooth label 2408, restoration label 2410, carieslabel 2412, and one or both of a style matrix 2424 and geography stylematrix 2426 for the labeler that generated the labels 2408, 2410, 2412,2420. Each training data entry may also include a target defect label2420 as the desired output of the training data entry. In this manner,the generator 2402 is trained to identify defects in restorations whiletaking into account variations in labeling behaviors of individuals andpopulations in a given geographic area.

FIG. 25 is a schematic block diagram of a system 2500 for selecting arestoration for a tooth in accordance with an embodiment of the presentinvention. The system 2500 includes a generator 2502 coupled to adiscriminator 2504. The generator 2502 may be an encoder-decoder and thediscriminator 2504 may be an encoder. The generator 2502 anddiscriminator 2504 may be implemented and trained using any of theapproaches described herein for implementing a generator anddiscriminator of a GAN, such as CNNs. Other machine learning approachesmay also be used, such as random forest, gradient boosting, or supportvector machine.

The generator 2502 takes as inputs an image 2506 and a tooth label 2508(e.g., pixel mask showing pixels representing a tooth). These inputs areconcatenated and processed using the generator 2502 to obtain asynthetic restoration label 2510, e.g. a pixel mask showing an area forwhich a restoration is estimated for the tooth represented by the label2508 and the input image represented by label 2506.

The synthetic restoration label 2510 may be input with a realrestoration label 2512 to the discriminator 2504. The real restorationlabel 2512 may be a pixel mask of the area occupied by one or morerestorations represented in an unpaired dental image (not the image 2506or an image of the same anatomy represented in the image 2506). Thesynthetic restoration label 2510 and real restoration label 2512 areinput to the discriminator 2504 that outputs a realism matrix 2514 suchthat each value of the realism matrix is an estimate as to which of thelabels 2510, 2512 is real.

The synthetic restoration label 2510 may also be compared to a targetrestoration label 2516 that is a pixel mask labeling the area occupiedby one or more restorations actually performed on the tooth labeled bythe tooth label 2508.

The result of this comparison is a generator loss 2518 that increaseswith increase in differences between the labels 2510, 2516. Accordingly,the generator 2502 may be trained by a training algorithm that adjuststhe generator 2502 to reduce the generator loss 2518 and to increase thelikelihood that the realism matrix 2514 will indicate that the syntheticrestoration labels 2510 are real. The training algorithm likewise trainsthe discriminator 2504 to correctly identify the synthetic restorationlabels 2510 as fake. Training may continue until the generator loss 2518converges and the discriminator 2504 cannot distinguish between thesynthetic and real restoration labels 2510, 2512 or Nash equilibrium isreached.

As shown in FIG. 25, training may additionally be performed withreference to an individual style matrix 2520 (style matrix for anindividual labeler) and/or a geography style matrix 2522 (style matrixfor labelers within a geographic region) of a training data entry. Thematrices 2520, 2522 may be obtained using the system 2200 for thelabeler that generated the target restoration labels 2516 for the images2506. The style matrices 2520, 2522 may be concatenated with one anotherand with an output of one of the stages of the generator 2502 and theresult of the concatenation may then be input to the next stage of thegenerator 2502. For example, the matrices 2520, 2522 may be concatenatedwith the output of the stage 2524 that is the last stage of the encoderand the first stage of the decoder of the generator 2502.

During training, each training data entry may therefore include asinputs an image 2506, tooth label 2508, and one or both of a stylematrix 2520 and geography style matrix 2522 for the labeler thatgenerated the labels 2508, 2516. Each training data entry may alsoinclude a target restoration label 2516 as the desired output for thetraining entry. In this manner, the generator 2502 is trained to selectan appropriate restoration for a tooth while taking into accountvariations in labeling behaviors of individuals and populations in agiven geographic area.

FIG. 26 is a schematic block diagram of a system 2600 for identifyingsurfaces of a tooth having caries in accordance with an embodiment ofthe present invention. Caries are often identified by evaluatingtwo-dimensional images, such as X-rays. It may not always be apparentfrom an X-ray which surface of a tooth bears a carious lesion. Forexample, an apparent carious lesion may be on the surface facing theviewer or away from the viewer.

The illustrated system 2600 may be used to estimate the surface of atooth in which caries are present. As known in the field of dentistry,these surfaces may be the mesial (facing forward), occlusal (chewingsurface), distal (facing rearward), buccal (facing toward the cheek),and lingual (facing toward the tongue) (designated herein as M, O, D, B,and L, respectively).

The system 2600 may include an encoder network 2602. The encoder network2602 may include a number of multi-scale stages with downsamplingbetween them with the last stage coupled to a fully connected layer. Theencoder network 2602 may be implemented according to any of theapproaches described above for implementing a CNN. Other machinelearning approaches may also be used, such as random forest, gradientboosting, or support vector machine.

Training data entries may each include an image 2604, such as an imageof dental anatomy according to any of the imaging modalities describedherein. Each training data entry may further include a tooth label 2606(pixel mask indicating portion of image 2604 representing a tooth),caries label 2608 (pixel mask indicating portions of the image 2604corresponding to one or more caries on the tooth indicated by the label2606), and a restoration label 2610 (pixel mask indicating portions ofthe image 2604 representing any previous restoration performed withrespect to the caries on the tooth represented by the label 2606).

The image 2604 and labels 2606-2610 may be concatenated and processedusing the encoder 2602. The encoder 2602 then generates an output 2612that is a surface label having one of five values, each corresponding toone of the five surfaces (M, O, D, B, L) of a tooth. Accordingly, eachtraining data entry may include an image 2604 and labels 2606-2610 asinputs. The desired output for each training data entry may be a surfacelabel indicating the surface (M, O, D, B, L) on which the cariesindicated in the label 2608 are formed. The training algorithm maytherefore train the encoder 2602 to output a surface label for cariesfor a given input image 2604 and corresponding labels 2606-2610corresponding to those caries.

FIG. 27 is a schematic block diagram of a system 2700 for selectingdental treatments in accordance with an embodiment of the presentinvention. Dental treatments may include such treatments as a crown,restoration (e.g., filling), monitoring, preventative care, root canaltherapy, scaling and root planing per tooth or by oral quadrant,extraction, orthodontic treatment addressing malocclusion, oral surgicalintervention, and prosthodontic treatment, and root canal therapy. Thesystem 2700 may also be used for selecting orthodontic treatments suchas described in U.S. Provisional Application Ser. No. 62/916,966 filedOct. 18, 2019, and entitled Systems and Methods for AutomatedOrthodontic Risk Assessment, Medical Necessity Determination, andTreatment Course Prediction.

The system 2700 may include an encoder network 2702. The encoder network2702 may include a number of multi-scale stages with downsamplingbetween them with the last stage coupled to a fully connected layer. Theencoder network 2702 may be implemented according to any of theapproaches described above for implementing a CNN. Other machinelearning approaches may also be used, such as random forest, gradientboosting, or support vector machine.

Training data entries may each include an image 2704, such as an imageof dental anatomy according to any of the imaging modalities describedherein. Each training data entry may further include a tooth label 2706(pixel mask indicating portion of image 2604 representing a tooth),caries label 2708 (pixel mask indicating portions of the image 2704corresponding to one or more caries on the tooth indicated by the label2706), and a restoration label 2710 (pixel mask indicating portions ofthe image 2704 representing any prior restoration performed with respectto the tooth indicated by the tooth label 2706)). In this manner,additional treatments needed to fix a prior restoration may beidentified.

The image 2704 and labels 2706-2710 may be concatenated and processedusing the encoder 2702. The encoder 2702 then generates an output 2712that is a treatment estimate, e.g. a numerical value corresponding to atreatment. Accordingly, each training data entry may include an image2704 and labels 2706-2710 as inputs. The desired output for eachtraining data entry may be a treatment option, e.g. the numerical valuecorresponding to the appropriate treatment option for the cariesindicated by the label 2708. The training algorithm may therefore trainthe encoder 2702 to output a treatment estimate for a given input image2704 and corresponding labels 2706-2710.

FIG. 28 is a schematic block diagram of a system 2800 for selecting adiagnosis, treatment, or patient match in accordance with an embodimentof the present invention. In particular, treatments may include aselection of a treatments for caries based on the extent and depth ofthe caries. Such treatments may include a filling, multiple fillings, acrown, restoration, monitoring, preventative care, root canal therapy,or extraction. As another example, the dental pathology may includeendodontic disease, e.g., carious lesions in bone such that a treatmentmay include tooth extraction. In another example, the presence of decayin bone around a tooth may be used to determine whether to do a crown,root canal, or extraction. In yet another example, decay around aprevious restoration (e.g., filling or crown) or treatment (e.g., rootcanal therapy) may be used to determine an appropriate additionaltreatment such as root canal therapy, extraction, or additional rootcanal therapy. The system 2800 may also be used for diagnosingorthodontic conditions and selecting orthodontic treatments such asdescribed in U.S. Provisional Application Ser. No. 62/916,966 filed Oct.18, 2019, and entitled Systems and Methods for Automated OrthodonticRisk Assessment, Medical Necessity Determination, and Treatment CoursePrediction.

The system 2800 may include an anatomy identification machine learningmodel 2802, which may be embodied by a CNN, such as an encoder-decoderCNN according to any of the embodiments disclosed herein. The machinelearning model 2802 may also be implemented using other machine learningapproaches such as such as random forest, gradient boosting, or supportvector machine.

The machine learning model 2802 takes as inputs an image 2804, which maybe an image corrected according to any of the approaches describedherein (reoriented, decontaminated, transformed, inpainted). The machinelearning model 2802 may further take as an input one or more anatomicalmasks 2806 for the image 2804. The anatomical masks 2806 may be pixelsmasks labeling anatomy represented in the image 2804. The anatomicalmasks 2806 may identify any of the dental anatomy described herein, suchas teeth, CEJ, GM, JE, bony points, caries, periapical line, or otherdental anatomy. The anatomical masks 2806 may label dental pathologiessuch as caries, carious lesions in bone, or other dental pathologies.The anatomical masks 2806 may label previous restorations such asfillings, crowns, root canal therapy, or other restorations. Theanatomical masks 2806 may be generated by a trained dental professionalor generated using a machine learning model trained and utilized asdescribed herein. Images 2804 and corresponding anatomical masks 2806may be generated and stored in a database for later processing using themachine learning model 2802 or other machine learning models describedherein.

The image 2804 and the one or more anatomical masks 2806 may beconcatenated and processed using the machine learning model 2802. Themachine learning model 2802 may be trained to output measurements 2808of the anatomy labeled by the masks. Accordingly, training data entriesmay each include an image 2804 and one or more anatomical masks 2806 asinputs and one or more measurements as desired outputs. The trainingalgorithm may then train the machine learning model 2802 to output ameasurement for a given input image 2804 and corresponding anatomicalmasks 2806.

The machine learning model 2802 may be multiple models, each beingtrained to output a particular measurement or group of measurements. Themeasurements of an item of anatomy may include its center of mass,relative distance to other anatomy, size distortion, and density.Measurements for caries may include volume, area, distance to pulp,percent of tooth covered by it, distance into dentin, involved surfacesof the tooth (M, O, D, B, L), and identifier of the affected tooth.Measurements of fillings or other restorations on teeth may includevolume, area, percent of tooth covered by it, involved surfaces of thetooth (M, O, D, B, L), material, type, and identifier of the affectedtooth. Measurements of periodontal anatomy may include distal gingivalmargin, mesial gingival margin, distal CAL, mesial CAL, distal PD,mesial PD, distal bone level, mesial bone level, and the identifier ofthe tooth for which the periodontal anatomy is identified and measured.Measurements relating to root canal therapy at a given tooth positionmay include crown-to-root-ratio, calculus, root length, relativedistance to adjacent teeth, furcation, fracture, and whether the toothat that tooth position is missing.

The measurements 2808 may then be processed by a machine learning model2810 to perform one or more tasks such as obtaining a diagnosis,determining an appropriate treatment, or identifying a patient thatmatches the measurements 2808. Identifying a matching patient may behelpful in claim adjudication to determine how a claim involving asimilar patient was decided.

In some embodiments, the machine learning model 2810 is a dense neuralnetwork including two layers. In some embodiments, the first layer has1000 parameters and the second network has 100 parameters. The head ofthe network (core model 2812) may be separate from the rest of thenetwork (task models 2814) and trained separately. Data may be processedby the core model 2812 followed by the output of the core model 2812being processed by the task models 2814, each task model 2814 outputtingan estimate 2816 corresponding to the task it is being trained toperform.

For example, the machine learning model 2810 may be trained according toa multitask training algorithm. The training algorithm may proceed asfollows:

(Step 1) The core model 2812 and a first task model 2814 are trained toperform the task corresponding to the first task model 2814 (treatmentidentification in the illustrated embodiment).

(Step 2) The other task models 2814 are trained to perform theircorresponding tasks one at a time without changing the core model 2812(diagnosis determination and patient matching models 2814 in theillustrated embodiment).

(Step 3) Each of the task models 2814 is trained individually againexcept that the training at this step includes further training of thecore model 2812.

(Step 4) The core model 2814 is trained to perform the taskscorresponding to each of the task models 184 in combination with thetask models 2814 except that only the core model 2812 is modified andthe task models 2814 are maintained fixed. Step 4 may include processingdata sets for each task in series. E.g., data set for task 1 isprocessed using the core model 2812 and the task model 2814 for task 1,the data set for task 2 is processed using the core model 2812 and thetask model 2814 for task 2, and so on for each task with only the coremodel 2812 being modified during the training.

For the treatment identification task, the training data entries mayeach include an image 2804 and anatomical masks 2806 as inputs and anappropriate treatment as determined by a dental professional as adesired output. Likewise, the training data entries for diagnosisdetermination may each include an image 2804 and anatomical masks 2806as inputs and an appropriate diagnosis as determined by a dentalprofessional as a desired output. For patient matching, training dataentries may each include an image 2804 and anatomical masks 2806 asinputs and a vector or matrix of characterizing values as a desiredoutput. Accordingly, the core model 2812 and task model 2814 for thepatient matching tasks may function as an autoencoder. The vector ormatrix of characterizing values being such that they may be compared toa database of patient records to identify another patient that has asimilar vector or matrix. Similarity may be measured using cosigndifference measurements or other approach.

Once trained, the system 2800 may be used to evaluated the impact ofperturbations to anatomical masks on the output of the machine learningmodel 2812. Specifically, one or more masks 2806 for an image 2804 maybe perturbed according to a first perturbation style (e.g., as definedby a perturbation matrix or a perturbation value processed by aperturbation algorithm to modify the mask 2806). The image 2804 andmasks 2806 having one or more masks replaced with the perturbed masksmay be processed using the machine learing model 2802 to obtainmeasurements 2808, which are then processed using machine learning model2810 to obtain first outputs for one or more tasks of the machinelearning model 2810.

The process of the preceding paragraph may be repeated for a secondperturbation style that is different from the first perturbation styleto obtain second outputs from the machine learning model 2810 for one ormore tasks of the machine learning model 2810. The user may then comparethe outputs for the first and second perturbations styles to determinehow the perturbation style impacts diagnosis determination, treatmentidentification, and/or patient matching.

In some embodiments a system may include an interface that may bedisplayed to a user and include user interface elements enabling theuser to adjust perturbation styles, such as amount of erosion ordilation or amount of boundary roughening or smoothing to apply. Thesystem may then generate a perturbation style corresponding to theamounts specified by the user and apply the perturbation style to ananatomical mask. The user may therefore experiment with perturbationstyles and determine how they affect diagnosis determination, treatmentidentification, and/or patient matching.

The interface may further provide interface elements allowing the userto individually specify the amounts of perturbation for each type ofanatomical mask 2806, e.g. each item of anatomy represented by one ofthe anatomical masks 2806. The user may therefore amplify or diminishthe impact of a particular anatomical mask 2806 on the output of themachine learning model 2810. For example, a user might find that if theychange the pulp, enamel, bone, gingival margin, CEJ, tooth, or cariesmasks, the output treatment, diagnosis, or patient match mightcorrespond better with the user's own stylistic preferences.

In some embodiments, a perturbation style selected by a user may beinput by concatenating a style matrix corresponding to the perturbationstyle with an inner stage of the machine learning model 2802, such asusing the approach described above with respect to FIG. 24.

FIG. 29 is a schematic block diagram of a system 2900 for predictingclaim adjudication according to a treatment plan in accordance with anembodiment of the present invention. The treatments for which a claimadjudication may be predicted may include any of the treatments for anyof the diagnosis of a dental, periodontal, or orthodontic condition,such as any of the treatments for any of the dental, periodontal, ororthodontic condition described herein.

Determining the most appropriate care for a dental patient is often abalance between competing objectives. A patient might present anatomynecessitating aggressive intervention, but the patient's dentalinsurance plan might only cover a less invasive procedure. To allocateclinical resources efficiently, it is often useful to know how aprocedure will be adjudicated by a payer network. Having clarity onpayer decision making would enable a more streamlined clinical workflow.However, payer claim adjudication decisions can change from day-to-day.Also, different payers have different adjudication tendencies andtimelines, which makes it very difficult for dentists to determineoptimal patient care.

To solve this problem, an automated treatment likelihood system 2900 maybe trained and used to predict payer decisions with respect to aparticular treatment. The system 2900 may include an anatomyidentification machine learning model 2802, which may be embodied by aCNN, such as an encoder-decoder CNN according to any of the embodimentsdisclosed herein. The machine learning model 2902 may also beimplemented using other machine learning approaches such as such asrandom forest, gradient boosting, or support vector machine.

The machine learning model 2802 takes as inputs an image 2904, which maybe an image corrected according to any of the approaches describedherein (reoriented, decontaminated, transformed, inpainted). In someembodiments, anatomical masks as described above with respect to thesystem 2800 are omitted. However, in other embodiments, the input to themachine learning model 2902 may include the image 2904 concatenated withone or more anatomical masks 2905.

The machine learning model 2902 may be trained to output measurements2906 of anatomy represented in the image 2904 and possibly theanatomical masks 2905 for the image 2904. The measurements may includesome or all of the measurements described above as being output by themachine learning model 2802. The machine learning model 2902 may betrained in the manner described above with respect to the machinelearning model 2802.

The measurements 2906 may be combined with one or more items of metadata2908 relating to the patient whose anatomy is represented in the image2904. The metadata may be in text form and may be extracted from patientrecords, such as clinical notes in patient records. The metadata mayinclude such information as age, comorbidities, past treatments, pastdiagnosis, past periodontal chart, past odontogram, geography,medications, other text notes, and past claims. The measurements 2906may also be combined with an identifier 2910 of a payer for whichtreatment likelihood is to be estimated.

The measurements 2906, metadata 2908, and payer identifier 2910 may beconcatenated and input to a machine learning model 2912. The machinelearning model may be trained to perform various tasks with respect tothe input data. The tasks may include treatment identification,diagnosis determination, and patient match identification as describedabove with respect to the system 2800. An additional task may includeclaim adjudication, e.g., a likelihood that a treatment identified willbe approved or disapproved by the entity identified by the payeridentifier 2910.

Accordingly, training data entries for the machine learning model 2912may include measurements 2906, metadata 2908, and a payer identifier2910 as inputs and as a desired output some or all of a treatmentidentification, diagnosis determination, patient match identification,and a claim adjudication. The claim adjudication may be binary(approved/disapproved) and/or a time value, e.g. an amount of timerequired before approval. The training algorithm may then train themachine learning model 2912 to perform the tasks using the training dataentries. The training algorithm may include performing the multitasktraining algorithm described above with respect to the machine learningmodel 2810. The machine learning model 2912 may include a core model andtask model for each tasks using the approach described above withrespect to the machine learning model 2810.

The machine learning model 2912 may be implemented as a neural networkcomprised of two dense layers, such as a fully connected network. Thenumber of parameters in each layer may vary depending on the type ofimaging modality and anatomical location. Feature distillation may beconducted prior to final training. The final output size may be variabledepending on whether the model 2912 is predicting treatment (Tx),diagnosis (Dx), closest historical patient match, or claimsadjudication. The fully connected network may be replaced other withmachine learning algorithms such a tree-based techniques, gradientboosting, and support vector machines. The alternative machine learningalgorithms may also be used in an ensemble method.

Following training, an image 2904 of a patient, and possibly anatomicalmasks 2905 for the image 2904 may be processed using the machinelearning model 2902 to obtain measurements. Measurements 2906, metadata2908 for the patient, and a payer identifier 2910 may then be processedusing the machine learning model 2912 to obtain some or all of atreatment identification, diagnosis determination, closest patientmatch, or a predicted claim adjudication. In some embodiments, thepredicted claim adjudication may include a predicted time beforeapproval.

As for the system 2800, the system 2900 may include an interface thatmay be displayed to a user and include user interface elements enablingthe user to adjust perturbation styles, such as amount of erosion ordilation or amount of boundary roughening or smoothing to apply. Thesystem may then generate a perturbation style corresponding to theamounts specified by the user and apply the perturbation style to animage. The user may therefore experiment with perturbation styles anddetermine how they affect diagnosis determination, treatmentidentification, patient matching, or claim adjudication.

The interface may further provide interface elements allowing the userto individually specify the amounts of perturbation for each anatomicalmask 2905. The user may therefore amplify or diminish the impact of aparticular anatomical mask 2905 on the output of the machine learningmodels 2902, 2912. For example, a user might find that if they changethe pulp, enamel, bone, gingival margin, CEJ, tooth, or caries detectionoutput then the treatment, diagnostic, patient match, or claimadjudication results might correspond better with their own stylisticpreferences.

Likewise, on a larger scale, a large number of patient data entries eachincluding an image 2904, anatomical masks 2905, patient metadata 2908,and payer identifier 2910 may be subject to a common perturbation styleof one or more masks 2905 to obtain claim adjudication predictions thatmay be aggregated (e.g., averaged or summed). This may be performedmultiple times with different perturbation styles for different types ofmasks 2905. A user may therefore estimate how a change in theperturbation style of a mask 2905 of a particular anatomical featurecould affect claim adjudications in aggregate. The user is therebyenabled to determine how perturbations to a mask 2905 of a particularanatomical feature affects risk of the payer or other party.

In some embodiments, a perturbation style selected by a user may beinput to the system 2900 by concatenating a style matrix correspondingto the perturbation style with an inner stage of the machine learningmodel 2802, such as using the approach described above with respect toFIG. 24.

Referring to FIG. 30, in some embodiments, a system 3000 may be used todetermine a likelihood of a treatment being appropriate. The treatmentsfor which likelihood of treatment may be predicted may include any ofthe treatments for any of the diagnosis of a dental, periodontal, ororthodontic condition, such as any of the treatments for any of thedental, periodontal, or orthodontic condition described herein.

The system 3000 may include a two-layer bi-directional long short-termmemory (LSTM) network 3002. The LSTM 3002 takes as inputs the outputs ofmachine learning models 2900 a-2900 d. Although four machine learningmodels 2900 a-2900 d are shown, the approach described herein may beused with any number of machine learning models 2900 a-2900 d greaterthan two. The machine learning models 2900 a-2900 d may be implementedas a system 2900 as described above except that one or more of the lastlayers of the machine learning model 2912 are removed and the outputs ofthe last remaining layer are then input to the LSTM 3002.

The machine learning models 2900 a-2900 c each take as inputs patientdata at for a dental appointment preceding a current claim for whichadjudication is being predicted. The patient data for an appointment mayinclude any of the data described above as being input to the machinelearning model 2902, such as an image captured during the appointment,anatomic labels for the image, patient metadata as constituted at thetime of the appointment. The machine learning model 2900 d takes asinput the same items of patient data from an appointment for which thelikelihood of a treatment is to be determined using the system 3000.

The LTSM 3002 may be trained with historical patient data to output atreatment likelihood 3006. In some embodiments, the treatment likelihood3006 may be an estimate of approval of payment for a treatment by apayer. Accordingly, an input to the LTSM 3002 may be a payer identifier3004. Accordingly, a training data entry for training the system 3000may include the patient data for a plurality of appointments (e.g. anumber of appointments equal to the number of machine learning models2900 a-2900 d) as an inputs and a treatment approved or denied for thelast appointment in the set of appointments as a desired output. Eachtraining data entry may further include a payer identifier 3004 for thepayer that approved or denied the treatment. The LTSM 3002 may then betrained by inputting the patient data for each appointment into one ofthe machine learning models 2900 a-2900 d. In some embodiments, temporalordering is preserved, e.g. machine learning model 2900 a receivespatient data for the earliest appointment, machine learning model 2900 bfor the next appointment, and so on to the last machine learning model2900 d which receives the patient data for the most recent appointment.The outputs of the machine learning models 2900 a-2900 d are processedusing the LSTM 3002 to obtain a treatment likelihood 3006. The trainingalgorithm then compares a treatment likelihood 3006 output by the LSTM3002 to the treatment approved or denied as recorded the training dataentry and updates the LSTM 3002 according to whether the treatmentlikelihood matches the treatment approved or denied as recorded in thetraining data entry.

In use, patient data for a set of appointments may then be input to themachine learning models 2900 a-2900 d as described above and the outputsof the machine learning models 2900 a-2900 d input to the LSTM 3002(possibly with a payment identifier 3004) to obtain a treatmentlikelihood 3006.

Various alternative embodiments are also possible. For example, in somecases there may be records of some or all of an actual diagnosis,treatment, and claim adjudication for prior appointments. This dataalong with other patient data (e.g., image, anatomical labels, oranatomy measurements) may be referred to as an appointment data set. TheLTSM 3002 may define inputs for a plurality of appointment data sets,with the input for the most recent appointment taking only patient datawithout data defining a claim adjudication. The LTSM 3002 may then betrained to determine a treatment likelihood, which may be a claimadjudication likelihood, or the last appointment.

As for other embodiments disclosed herein, an interface may be providedto evaluate the impact of perturbations to anatomical labeling on thetreatment likelihood 3006. Perturbations for an anatomical label type asinput by a user may be implemented with respect to the machine learningmodels 2900 a-2900 d as described above with respect to the system 2900.This may include evaluating a financial implication of perturbations onan aggregation of treatment likelihoods for patient data from a large(e.g., 100 s or 1000 s) set of patients.

FIG. 31 is a block diagram illustrating an example computing device 3100which can be used to implement the system and methods disclosed herein.In some embodiments, a cluster of computing devices interconnected by anetwork may be used to implement any one or more components of theinvention.

Computing device 3100 may be used to perform various procedures, such asthose discussed herein. Computing device 3100 can function as a server,a client, or any other computing entity. Computing device can executeone or more application programs, such as the training algorithms andutilization of machine learning models described herein. Computingdevice 3100 can be any of a wide variety of computing devices, such as adesktop computer, a notebook computer, a server computer, a handheldcomputer, tablet computer and the like.

Computing device 3100 includes one or more processor(s) 3102, one ormore memory device(s) 3104, one or more interface(s) 3106, one or moremass storage device(s) 3108, one or more Input/Output (I/O) device(s)3110, and a display device 3130 all of which are coupled to a bus 3112.Processor(s) 3102 include one or more processors or controllers thatexecute instructions stored in memory device(s) 3104 and/or mass storagedevice(s) 3108. Processor(s) 3102 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 3104 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM) 3114) and/ornonvolatile memory (e.g., read-only memory (ROM) 3116). Memory device(s)3104 may also include rewritable ROM, such as Flash memory.

Mass storage device(s) 3108 include various computer readable media,such as magnetic tapes, magnetic disks, optical disks, solid-statememory (e.g., Flash memory), and so forth. As shown in FIG. 31, aparticular mass storage device is a hard disk drive 3124. Various drivesmay also be included in mass storage device(s) 3108 to enable readingfrom and/or writing to the various computer readable media. Mass storagedevice(s) 3108 include removable media 3126 and/or non-removable media.

I/O device(s) 3110 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 3100.Example I/O device(s) 3110 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Display device 3130 includes any type of device capable of displayinginformation to one or more users of computing device 3100. Examples ofdisplay device 3130 include a monitor, display terminal, videoprojection device, and the like.

A graphics-processing unit (GPU) 3132 may be coupled to the processor(s)3102 and/or to the display device 3130, such as by the bus 3112. The GPU3132 may be operable to perform convolutions to implement a CNNaccording to any of the embodiments disclosed herein. The GPU 3132 mayinclude some or all of the functionality of a general-purpose processor,such as the processor(s) 3102.

Interface(s) 3106 include various interfaces that allow computing device3100 to interact with other systems, devices, or computing environments.Example interface(s) 3106 include any number of different networkinterfaces 3120, such as interfaces to local area networks (LANs), widearea networks (WANs), wireless networks, and the Internet. Otherinterface(s) include user interface 3118 and peripheral device interface3122. The interface(s) 3106 may also include one or more user interfaceelements 3118. The interface(s) 3106 may also include one or moreperipheral interfaces such as interfaces for printers, pointing devices(mice, track pad, etc.), keyboards, and the like.

Bus 3112 allows processor(s) 3102, memory device(s) 3104, interface(s)3106, mass storage device(s) 3108, and I/O device(s) 3110 to communicatewith one another, as well as other devices or components coupled to bus3112. Bus 3112 represents one or more of several types of busstructures, such as a system bus, PCI bus, IEEE 1394 bus, USB bus, andso forth.

For purposes of illustration, programs and other executable programcomponents are shown herein as discrete blocks, although it isunderstood that such programs and components may reside at various timesin different storage components of computing device 3100, and areexecuted by processor(s) 3102. Alternatively, the systems and proceduresdescribed herein can be implemented in hardware, or a combination ofhardware, software, and/or firmware. For example, one or moreapplication specific integrated circuits (ASICs) can be programmed tocarry out one or more of the systems and procedures described herein.

The invention claimed is:
 1. A method for training a machine learningmodel comprising: providing, on the computer system, a plurality offirst dental images; providing, on a computer system, a random mask foreach image of the plurality of first dental images; processing, by thecomputer system, each image of the plurality of first dental images andthe random mask for the each image with a generator machine learningmodel to obtain an inpainted image for the each image, the inpaintedimage including image data generated by the generator machine learningmodel for image locations corresponding to the random mask for the eachimage; and for each image of the plurality of first dental images,updating, by the computer system, the generator machine learning modelaccording to a training algorithm according to similarity of the eachimage to the inpainted image obtained for the each image; wherein eachimage of the plurality of first dental images is an image of dentalanatomy according to an imaging modality selected from the groupconsisting of full mouth series X-rays, dental cone beam computedtomography (CBCT), cephalometric X-ray, intra-oral optical image,panoramic dental X-ray, dental magnetic resonance imaging (MRI) image,dental light detection and ranging (LIDAR) image.
 2. The method of claim1, further comprising: processing, by the computer system, the inpaintedimage for each first dental image of the plurality of first dentalimages and an unpaired dental image from a repository using adiscriminator machine learning model to obtain a realism estimate; andupdating, by the training algorithm, the generator machine learningmodel and the discriminator machine learning model according to therealism estimates for the plurality of first dental images.
 3. Themethod of claim 2, wherein the generator machine learning model is anencoder-decoder machine learning model.
 4. The method of claim 3,wherein the generator machine learning model and the discriminatormachine learning model are each convolution neural networks.
 5. Themethod of claim 2, wherein the realism estimate is a matrix of realismestimates.
 6. The method of claim 1, further comprising: processing, bythe computer system, a second dental image using the generator machinelearning model to obtain a utilization inpainted image; and processing,by the computer system, the utilization inpainted image according to alabeling machine learning model to obtain labels of dental anatomy inthe utilization inpainted image.
 7. The method of claim 6, whereinprocessing the second dental image using the generator machine learningmodel comprises processing the second dental image in combination with amask indicating missing portions of the second dental image.
 8. Themethod of claim 6, further comprising: processing, by the computersystem, the utilization inpainted image and labels of dental anatomyaccording to a measuring machine learning model to obtain a measurementof a dental condition.
 9. The method of claim 8, further comprising:processing, by the computer system, the utilization inpainted image,labels of dental anatomy, and measurement of the dental conditionaccording to a diagnosing machine learning model to obtain a diagnosisof a dental pathology.
 10. The method of claim 9, wherein the dentalpathology is a diagnosis of a periodontal disease.
 11. A non-transitorycomputer-readable medium storing executable instructions that, whenexecuted by a processing device, cause the processing device to: receivea plurality of first dental images; receive or generate a random maskfor each image of the plurality of first dental images; process eachimage of the plurality of first dental images and the random mask forthe each image with a generator machine learning model to obtain aninpainted image for the each image, the inpainted image including imagedata generated by the generator machine learning model for imagelocations corresponding to the random mask for the each image; and foreach image of the plurality of first dental images, update the generatormachine learning model according to a training algorithm according tosimilarity of the each image to the inpainted image obtained for theeach image; wherein each image of the plurality of first dental imagesis an image of dental anatomy according to an imaging modality selectedfrom the group consisting of full mouth series X-rays, dental cone beamcomputed tomography (CBCT), cephalometric X-ray, intra-oral opticalimage, panoramic dental X-ray, dental magnetic resonance imaging (MRI)image, dental light detection and ranging (LIDAR) image.
 12. Thenon-transitory computer-readable medium of claim 11, wherein theexecutable instructions, when executed by the processing device, furthercause the processing device to: process the inpainted image for eachfirst dental image of the plurality of first dental images and anunpaired dental image from a repository using a discriminator machinelearning model to obtain a realism estimate; and update, by the trainingalgorithm, the generator machine learning model and the discriminatormachine learning model according to the realism estimates for theplurality of first dental images.
 13. The non-transitorycomputer-readable medium of claim 12, wherein the generator machinelearning model is an encoder-decoder machine learning model.
 14. Thenon-transitory computer-readable medium of claim 13, wherein thegenerator machine learning model and the discriminator machine learningmodel are each convolution neural networks.
 15. The non-transitorycomputer-readable medium of claim 12, wherein the realism estimate is amatrix of realism estimates.
 16. The non-transitory computer-readablemedium of claim 11, wherein the executable instructions, when executedby the processing device, further cause the processing device to:process a second dental image using the generator machine learning modelto obtain a utilization inpainted image; and process the utilizationinpainted image according to a labeling machine learning model to obtainlabels of dental anatomy in the utilization inpainted image.
 17. Thenon-transitory computer-readable medium of claim 16, wherein theexecutable instructions, when executed by the processing device, furthercause the processing device to process the second dental image using thegenerator machine learning model by processing the second dental imagein combination with a mask indicating missing portions of the seconddental image.
 18. The non-transitory computer-readable medium of claim16, wherein the executable instructions, when executed by the processingdevice, further cause the processing device to: process the utilizationinpainted image and labels of dental anatomy according to a measuringmachine learning model to obtain a measurement of a dental condition.19. The non-transitory computer-readable medium of claim 18, wherein theexecutable instructions, when executed by the processing device, furthercause the processing device to: process the utilization inpainted image,labels of dental anatomy, and measurement of the dental conditionaccording to a diagnosing machine learning model to obtain a diagnosisof a dental pathology.
 20. The non-transitory computer-readable mediumof claim 19, wherein the dental pathology is a diagnosis of aperiodontal disease.